Anais da Conference on Graphics, Patterns and Images (SIBGRAPI)
Regularization for Inverse Problems and Machine Learning: A Tutorial
In this tutorial, we explore different paradigms for solving image processing tasks such as denoising and deblurring. These tasks can be interpreted as ill-posed inverse problems, grounded in physical-mathematical models that require regularization to produce meaningful solutions. Alternatively, they can be framed as supervised regression tasks within a machine learning context, where regularization techniques are used to improve generalization to unseen data. We compare these two perspectives, highlighting their similarities and dif-ferences-particularly in how regularization is understood and applied in each framework.
Tiny Titans: Efficient Large Vision, Language and Multimodal Models Through Pruning
Notable progress in solving complex reasoning tasks relies on large models. Unfortunately, developing these models demands substantial computational resources and energy consumption. Hence, the industry pushes the most significant advances in state-of-the-art models and draws the attention of the scientific community to the environmental impact of AI (GreenAI). Pruning emerges as an effective mechanism to address the capacity-computational cost dilemma by eliminating structures (weights, neurons or layers) from deep models. This tutorial introduces theoretical and technical foundations within this promising, active and exciting field. It delves into pruning techniques as a pillar of GreenAI and a foundation for the next wave of efficient large vision, language, and multimodal models. Our tutorial also covers how existing forms of pruning impact efficiency gains, guiding participants to make informed choices for their scenario and infrastructure. Specifically, we equip participants with the basics and key recipes to effectively apply pruning in practical computer vision scenarios. Additional material is available at: github.com/arturjordao/TinyTitans
From Volume Rendering to 3D Gaussian Splatting: Theory and Applications
The problem of 3D reconstruction from posed images is undergoing a fundamental transformation, driven by continuous advances in 3D Gaussian Splatting (3DGS). By modeling scenes explicitly as collections of 3D Gaussians, 3DGS enables efficient rasterization through volumetric splatting, offering thus a seamless integration with common graphics pipelines. Despite its real-time rendering capabilities for novel view synthesis, 3DGS suffers from a high memory footprint, the tendency to bake lighting effects directly into its representation, and limited support for secondary-ray effects. This tutorial provides a concise yet comprehensive overview of the 3DGS pipeline, starting from its splatting formulation and then exploring the main efforts in addressing its limitations. Finally, we survey a range of applications that leverage 3DGS for surface reconstruction, avatar modeling, animation, and content generation—highlighting its efficient rendering and suitability for feed-forward pipelines.
The Flow of Creation: A Tour of Flow Matching for Visuals
Flow Matching has recently emerged as an efficient alternative to the generative method paradigms. Here we aim to provide researchers and practitioners with both the theoretical and the practical aspects of this technique. We delve into the continuous formulation, where a neural network learns a vector field to transform noise into data via an ordinary differential equation, and also explore its discrete counterpart. The paper covers the entire workflow, from the core mathematical concepts and training objectives to sampling procedures, including classifierfree guidance and conditioning generation. By showcasing a diverse range of applications—from image synthesis and human motion generation to computational biology and robotics—this work equips readers with the essential knowledge to apply and innovate with the versatile and computationally efficient flow matching framework.
Symmetry is a fundamental and pervasive property found in both natural and man-made objects, playing a key role in aesthetics, structure, and function. In computational domains, symmetry serves as a powerful cue for data compression, structure inference, and shape understanding. This work presents a comprehensive overview of symmetry analysis in 3D shapes, with a particular focus on computational methods for symmetry detection and their applications in diverse fields such as CAD, computer vision, medicine, archaeology, and 3D modeling. We provide formal definitions of exact, approximate, and partial symmetries in the context of rigid transformations, and we survey five major categories of detection approaches: transformation-based, correspondence-based, voting-based, optimization-based, and learning-based methods. Special emphasis is placed on recent deep learning techniques, which have significantly advanced the state of the art yet face challenges in generalization and robustness. Finally, we identify key open problems and future directions, including the need for richer and more varied datasets, better generalization of learning-based models, effective formulations for symmetry detection in incomplete data, and the integration of symmetry priors in generative modeling. Our analysis highlights both the progress and the limitations of current methods and aims to guide future research toward more principled and capable symmetry-aware systems.
Getting Started with Semantic Segmentation in PyTorch Using SMP
Semantic segmentation is a core task in computer vision, essential for applications requiring detailed scene understanding, such as medical imaging, precision agriculture, and remote sensing. Recent advances in deep learning have significantly enhanced segmentation performance, particularly through encoder-decoder architectures combined with transfer learning. This tutorial provides a practical introduction to semantic segmentation using the Segmentation Models PyTorch (SMP) library, a widely adopted framework that integrates state-of-the-art architectures with pretrained encoders in an accessible interface. We offer a comprehensive overview of key concepts, supported model architectures, loss functions, evaluation metrics, and training strategies, emphasizing transparency and flexibility through native PyTorch implementations. To reinforce the concepts, we present two case studies: binary segmentation with the 38-Cloud dataset and multiclass segmentation with the DeepGlobe dataset. Both illustrate real-world applications, model configuration, preprocessing, and performance evaluation. All tutorial materials, including source code and reproducible experiments, will be made publicly available. The goal is to equip participants with practical knowledge to design, train, and evaluate semantic segmentation models effectively in a variety of domains.
Rank-Based Unsupervised Similarity Learning: Framework and Applications
Multimedia data collections have grown exponentially due to advances in acquisition and sharing technologies, creating a pressing need for robust similarity learning methods that do not depend on costly annotations. Deep features extracted by convolutional and transformer networks provide powerful embeddings, but often lie on complex manifolds that simple pairwise comparisons fail to capture. Unsupervised similarity learning addresses this gap by post-processing these embeddings with rank-based strategies that leverage the ordering of neighbors and manifold geometry through different approaches, e.g., graph and hypergraph constructions. This paper discusses foundational concepts in contextual similarity and presents a comprehensive overview of rank-based methods for unsupervised distance and similarity learning. It discusses the Unsupervised Distance Learning Framework (UDLF), along with its Python wrapper, pyUDLF, and the web-based interface UDLFWeb, which automates and simplifies the processes of experimentation and result visualization. We also show how these tools enhance the effectiveness of image retrieval, classification, and clustering, and outline future directions for expanding rank-based methods to additional modalities and scaling them to large datasets. The complete source code and documentation for UDLF and its associated tools are publicly available at: udlf.lucasvalem.com.
Strategies for Deep Learning in Volumetric Medical Imaging: A Survey
Deep learning has transformed medical image analysis. However, building effective models for volumetric data, such as CT, MRI, and PET scans, presents a new set of challenges. These include high computational costs, limited annotated datasets, and the need for architectures that can process volumetric data directly. This survey provides a comprehensive overview of recent advances in deep neural network architectures specifically designed for 3D medical imaging. We analyze the progression of architectural choices along the years, highlighting their innovations and applicability. In addition, we review training strategies such as supervised/self-supervised pretraining and the development of general-purpose 3D foundational models. Also, a comprehensive overview of 3D medical imaging datasets and their associated tasks is presented in the supplementary materials, highlighting the diverse clinical objectives that deep learning models can support across healthcare and diagnostic applications. A dedicated section addresses the emerging field of explainability in volumetric contexts, emphasizing the limitations of adapting 2D explainability-focused tools and the importance of 3D-native explanation frameworks. We conclude by outlining the key trends in the field of 3D medical imaging.
Facial Animation with GANs: Enhancing Temporal Coherence in Emotion Synthesis
Generative Adversarial Networks (GANs) have made significant advancements in generating high-quality facial animations, transforming static images into dynamic expressions. However, a persistent challenge in facial animation is ensuring temporal coherence-smooth and consistent transitions between frames. This issue often leads to visual artifacts and unnatural motion, hindering the realism of animated facial expressions. In this paper, we introduce a novel method to improve temporal coherence in GAN-based facial animation. By incorporating a specialized temporal consistency module and a recurrent loss function, our approach reduces abrupt transitions and enhances the fluidity of facial expression synthesis. We present both quantitative and qualitative evaluations of our method, which reduces temporal artifacts by 58% (TCM) and improves PSNR by 3.3 dB over baselines. We validate our approach across three facial expression datasets, demonstrating perceptual gains in temporal coherence, essential for robust emotion-driven character animation pipelines.
SkimCap: A Transformer-Based Video Captioning Method with Adaptive Attention and Hierarchical Skimming Features
We present SkimCap, a transformer-based video captioning framework that integrates a memory-augmented architecture with adaptive attention and a novel feature selection strategy grounded in hierarchical video skimming. Unlike traditional approaches that rely on uniformly sampled frames or pre-defined temporal segments, SkimCap performs unsupervised hierarchical clustering to identify and extract semantically salient video shots. These condensed representations provide a compact yet information-rich input to the captioning model, enabling more accurate and contextually grounded sentence generation. The memory module enhances long-range dependency modeling, while adaptive attention improves temporal alignment between visual cues and generated tokens. We evaluate SkimCap on ActivityNet, achieving CIDEr-D of 25.44, a BLEU-4 (B@4) of 10.77, and a lower Repetition-4 (R@4) score of 5.84, representing consistent caption quality and relevance improvements. An ablation study confirms the effectiveness of hierarchical skimming as a feature selection mechanism, highlighting its contribution to overall performance. SkimCap sets a new direction for incorporating structured visual summarization into end-to-end captioning systems.
Saliency-Guided Model with Cross and Channel Attention for Few-Shot Semantic Segmentation
Few-shot semantic segmentation (FSS) aims to segment novel classes in query images given only a few annotated support examples. This task remains challenging due to the limited supervision available during adaptation. In this work, we propose the Saliency-guided Model with Cross and Channel Attention (SaMoCCA) for FSS, a novel architecture that enhances support guidance using class-agnostic saliency maps and performs dense alignment between query and support features through multi-layer cross-attention. Saliency cues are extracted using a pretrained model and fused with the support mask to highlight discriminative regions. The resulting attention-weighted features are refined hierarchically via multi-scale aggregation blocks, squeeze-and-excitation modules, and a final mask-feature mixer that restores spatial details using high-resolution encoder features. In summary, the major contributions of this work are the use of saliency maps to guide the support branch and the adoption of channel attention mechanism to adaptively recalibrate features. Our findings confirm that both the saliency-guided mask and the squeeze-and-excitation block contribute positively and complement each other. Extensive experiments on the PASCAL-5 i and COCO-20 i benchmarks demonstrate that the proposed approach achieves competitive results compared to recent state-of-the-art methods, while offering architectural simplicity, strong generalization to unseen classes, and also demonstrates robust cross-domain generalization from COCO-20i to PASCAL-5 i without domain adaptation, highlighting the transferability of saliency-guided cross-attention.
Curriculum-Based Meta-Pseudo-Labeling on 2D Projections for Active Semi-Supervised Learning
Semi-supervised learning (SSL) is an effective approach to addressing the scarcity of accurately labeled data, leveraging both labeled and unlabeled samples for deep model training. However, limited labeled data can constrain its effectiveness. Active learning (AL) mitigates this by selecting informative samples for human annotation, enhancing SSL performance. Most state-of-the-art AL and SSL methods depend on pre-trained features and large validation sets for learning representations in classification tasks. We introduce contrastive active Deep Feature Annotation (ca-DeepFA), a method that integrates contrastive learning, active learning, and curriculum-based meta-pseudolabeling to train non-pre-trained CNNs for image classification with minimal labeled data and abundant unlabeled samples. The process begins with unsupervised contrastive pre-training on a small labeled set. At regular epoch intervals, label propagation is applied to 2D deep feature projections. Following this, pseudolabels are selected under a curriculum-driven policy, while an oracle annotates the most informative samples. These contribute to a composite loss function that combines supervised contrastive, supervised, and semi-supervised components-leading to enhanced feature representations for image classification. Evaluated on three real-world biological image datasets with a limited amount of labeled data, our method consistently outperforms baselines and state-of-the-art approaches by improving generalization and reducing annotation effort.
Isolating Vocalizations Beats Denoising: Spectrogram Granularity and Preprocessing in Ecoacoustic Classification
Passive Acoustic Monitoring (PAM) offers invaluable ecological insights, but classifying animal vocalizations via deep learning is severely hampered by pervasive noise, overlapping sounds, and critical mislabeling in manually annotated training data. This study systematically evaluates the impact of spectrogram generation strategies, diverse background noise reduction techniques, and a novel approach to identify and remove potentially mislabeled training instances on ecoacoustic classification performance. Experiments with ResNet and ViT architectures reveal that spectrograms with tightly bounded timefrequency windows around vocalizations dramatically improve classification. Surprisingly, both background noise removal and the exclusion of potentially noisy training labels yielded only a limited impact. These findings critically redirect future ecoacoustic efforts towards optimizing vocalization isolation.
Graph-Based Indexing for Fingerprint Identification Systems
Automated fingerprint identification systems traditionally compare a query against all stored records to identify a match, resulting in a computational burden that is impractical for large-scale fingerprint databases. To address this challenge, indexing techniques are crucial for reducing the search space and accelerating the identification process. This paper introduces a flexible fingerprint indexing framework based on Hierarchical Navigable Small World (HNSW) graphs, a state-of-the-art algorithm for approximate nearest neighbor search. The strength of HNSW relies on its ability to operate beyond metric spaces by supporting arbitrary similarity functions. We demonstrate such versatility through three distinct experimental setups: (1) indexing standard global feature vectors, (2) organizing an extensive collection of local, minutiae-based descriptors aggregated via majority voting, and (3) directly indexing proprietary biometric templates using the native, non-metric matching score of a Commercial-Off-The-Shelf (COTS) system. Our results show that HNSW significantly reduces similarity computations while maintaining high accuracy across all scenarios. Most notably, it minimizes the number of costly COTS matcher evaluations, establishing HNSW as a robust and adaptable solution for automated fingerprint identification systems.
A Parameter Estimation-Inspired Convolutional Block for SAR Data
Convolutional neural networks (CNNs) have been widely used in the context of SAR imagery analysis. However, most of the architectural choices involved in designing CNNs are based on trial-and-error with with limited grounding in formal mathematical principled decision-making. Here, we aim to bridge the gap between traditional moment-based approaches for SAR image processing and modern convolution-based learning algorithms. Specifically, we propose a simplified version of the traditional convolution operation, composed now of an average pooling layer for (first-order) moment computation and a series of 1×1 convolutional layers for learning nonlinear transformations of these moments. In practice, we show that traditional segmentation CNNs incorporating these simplified convolutional layers achieve similar results to their original counterparts while requiring fewer parameters, resulting in reduced computational and memory overhead.
A Deep Learning Framework for Pulmonary Disease Classification Using Volume-Rendered CTs
Accurate classification of pulmonary diseases is critical for clinical decision-making, and deep learning models using chest CT scans have become a key tool in this task. Most existing approaches rely on 2D CT slices, which provide limited views and may miss important spatial patterns across the lung volume. To address this, we introduce CT-VR, a novel classification approach that leverages 3D volume-rendered images captured from multiple angles. By incorporating multi-view volume rendering, CT-VR enhances the model ability to detect and differentiate between pulmonary conditions. We evaluate the method using COVID-19 datasets as a primary case study, which include private datasets from partner hospitals and a publicly available benchmark. Results demonstrate that our approach improves lesion identification and delivers performance compared to traditional slice-based models, highlighting its potential as a more effective solution for lung disease classification.
Uncertainty-Aware Chemical Analyses via Monte Carlo Deep Model Inference
We present a computer vision-based solution for estimating Chloride concentration and pH in water samples. The proposed chemical methodology produces samples with visually distinguishable reactions. Images are captured in a controlled lighting environment, and preprocessing techniques are applied to segment the Region of Interest (ROI). Each ROI is processed by a Convolutional Neural Network (CNN), which extracts highlevel descriptors. From each input sample, a total of n×n CNN-based descriptors (one per pixel in the spatially aligned feature map) yield a high-dimensional representation of local sample characteristics. They are input into a Neural Network model. After training, the model can infer a predictive distribution for each input image. This allows for the computation of descriptive statistics that characterize the associated uncertainty of the model's predictions. Our approach is inspired by the Monte Carlo method: descriptors are treated as independent observations drawn from a latent feature distribution. Although they are deterministically extracted from activation maps, they result from random visual outcomes of the same underlying analytical parameter. Conversely, they are not independent. The proposed approach can be applied to several tasks, including quality assurance, process monitoring, and compliance verification.
Data Augmentation Improves Machine Unlearning
Machine Unlearning (MU) aims to remove the influence of specific data from a trained model while preserving its performance on the remaining data. Although a few works suggest connections between memorisation and augmentation, the role of systematic augmentation design in MU remains underinvestigated. In this work, we investigate the impact of different data augmentation strategies on the performance of unlearning methods, including SalUn, Random Label, and Fine-Tuning. Experiments conducted on CIFAR-10 and CIFAR-100, under varying forget rates, show that proper augmentation design can significantly improve unlearning effectiveness, reducing the performance gap to retrained models. Results showed a reduction of up to 40.12% of the Average Gap unlearning Metric, when using TrivialAug augmentation. Our results suggest that augmentation not only helps reduce memorization but also plays a crucial role in achieving privacy-preserving and efficient unlearning.
Baking Reflections with 3D Gaussian Splatting in Real Time Ray-Tracing Rendering
This paper presents a method for rendering static reflections using precomputed 3D Gaussian Splatting (3DGS) models, aiming to enhance conventional rendering pipelines with realistic and efficient reflections. Unlike traditional techniques such Environment Mapping, which suffer from occlusion limitations and static viewpoint assumptions, our approach decouples reflection generation from the main rendering process by sampling color and radiance from 3DGS models organized in an octree structure. By constraining the number of Gaussians per node, our method allows for scalable control over quality and performance, achieving real-time frame rates in several synthetic and real-world scenarios. We show that even with simplified models, the reflections remain visually plausible, particularly in scenes with diffuse materials. Our results demonstrate accurate parallax and off-camera content reproduction, with potential applications in games, mixed reality, and offline rendering.
A Novel Method for Detecting 3D Steel Structures in As-Built Point Clouds
Building Information Modeling (BIM) integrates engineering data with 3D geometry, spatial relationships, and other properties to support construction projects throughout their entire lifecycle. Researchers have been focusing on how to automatically generate or update these models from 3D point clouds, considering existing buildings. Most of these studies have focused on recognizing planar structures (such as floors and walls) or cylindrical ones (such as pipelines). Only a few recent works have addressed the detection of steel structural elements, due to their particular geometry. In these approaches, the point cloud of each structural element was manually separated from the point cloud of the entire building. This practice poses a challenge, as manual segmentation of point clouds is a timeconsuming and subjective process. In this paper, we propose a new approach to automatically detect structural steel elements in 3D point clouds of existing buildings projects, without the need for prior segmentation and regardless of orientation. The proposed technique combines geometry processing algorithms with a machine learning strategy. The performance results demonstrate the effectiveness of our approach in detecting the desired elements. These results open up new possibilities for developing automated pipelines aimed at generating highly accurate 3D BIM models.
ResUNet: A Lightweight Residual U-Net for Scalable Fingerprint Enhancement
Enhancing latent fingerprints is a critical step in forensic biometric pipelines that remains challenging due to severe image degradation and the need for scalable processing. In this work, we introduce ResUNet, a lightweight Residual U-Net architecture for fingerprint enhancement that achieves competitive identification accuracy while remaining highly efficient. Our framework leverages a novel synthetic latent fingerprint generator designed to simulate realistic noise, occlusions, and background patterns for self-supervised learning. By segmenting recoverable regions with FingerNet and excluding unrecoverable areas from supervision, our model avoids hallucination, an issue prevalent in prior methods. We evaluate our method on the NIST SD27 dataset under a closed-set identification protocol using a 20,473-image gallery. Our approach outperforms recent state-of-the-art lightweight models by 6% Rank-1 accuracy while being up to 180× faster than transformer-based and GAN-based solutions. We also release our synthetic generation framework to promote reproducibility and benchmarking. These results show that ResUNet is a scalable and reliable solution for operational AFIS.
Optimizing Image Identification: Discriminative Keypoint Detection with Equivariant CNN and SSIM-Based Triplet Loss
Image identification becomes particularly challenging in datasets characterized by high intra-class similarity and minimal structural variation, such as wood textures, security paper, or metal alloys. In these contexts, effective discrimination depends on capturing fine-grained textural cues. We address this with a novel keypoint detector built upon an Equivariant Convolutional Neural Network, trained using a triplet loss function guided by the Structural Similarity Index. This design encourages the extraction of features that are not only equivariant to common transformations but also highly discriminative across visually similar instances. A central contribution of our method is the generation of keypoints with high repeatability - an attribute we show to be closely tied to improved identification accuracy. Through comprehensive experiments, we demonstrate that our approach consistently outperforms state-of-the-art methods in both matching and identification tasks across multiple datasets.
PSHDR: HDR Image Reconstruction from a Single-Exposure LDR Using Residual Features
Recent advances in deep learning techniques have led to the development of new approaches for reconstructing high dynamic range (HDR) images from a single low dynamic range (LDR) image. However, the images reconstructed by these methods show a loss of information in overexposed and underexposed regions. To overcome this challenge, this paper presents a deep neural network architecture designed to reconstruct HDR images from a single LDR image, with the aim of restoring information in saturated regions. The proposed architecture uses an encoder-decoder network that incorporates the Deformable Convolution Residual Block (DCRB) and Residual Feature Block (RFB) modules to restore information in saturated regions and a conditional network to modulate the information extracted from the input LDR image. To validate the proposed architecture, an experimental study was conducted using the NTIRE 2021 dataset, and the results were compared to 3 state-of-the-art algorithms. The results of the quantitative and qualitative analyses show that the proposed architecture obtains the best results among the compared approaches in the task of reconstructing singleexposure HDR images.
Simultaneous Learning Loss for Improved Cross-Domain Knowledge Transfer
Leveraging auxiliary data to improve performance on a target task is a potent strategy, yet it is often hindered by negative transfer, where domain misalignment degrades accuracy. We introduce Simultaneous Learning Loss (SLL), an objective function designed to enable robust cross-domain knowledge transfer by explicitly regularizing the shared representation space. SLL integrates a domain-balancing term with an InterGroup Penalty (GP), which directly discourages feature-level confusion between target and auxiliary domains without requiring architectural changes. We conduct extensive experiments on 12 cross-domain dataset pairs, using four target benchmarks and seven lightweight architectures. SLL consistently outperforms standard cross-entropy and multi-task learning baselines, achieving accuracy gains of up to 20.58 percentage points and demonstrating superior robustness against negative transfer. Our work establishes that explicitly penalizing inter-domain confusion is a powerful and generalizable principle for improving knowledge transfer.
PavicHDR: An Efficient Architecture for HDR Image Reconstruction Based on CNN and Attention Mechanisms
High dynamic range (HDR) image reconstruction is a technique that consists of merging multiple low dynamic range (LDR) images of the same scene in order to increase the quality of the image. One of the main challenges in this process is the movement present in each frame, which can generate ghosting artifacts in the resulting image. This paper proposes the PavicHDR architecture, designed to eliminate ghosting due to movement while maintaining a low computational cost. To do this, efficient feature extraction, alignment, refinement and fusion techniques were used. The quantitative and qualitative experiments carried out with the Kalantari and Tel datasets show the superior performance of PavicHDR in terms of reconstruction quality, reduction of distortions and preservation of structures. Moreover, it stands out for its reduced inference time compared to the state-of-the-art, including methods based on diffusion, vision transformers and adversarial neural networks.
A Deformable Convolutional Neural Network with Dual Attention for Multi-Exposure HDR Reconstruction
The reconstruction of High Dynamic Range (HDR) images from multiple Low Dynamic Range (LDR) images with varying exposures allows for the recovery of details in regions of a scene that are either very bright or very dark. However, factors such as movement, camera instability, and sudden changes in lighting can cause misalignment and visual artifacts in the reconstructed HDR image, representing important challenges in the field. In this context, this paper presents a novel model for multi-exposure HDR reconstruction that integrates specialized sub-networks for feature extraction and enhancement, alignment, and fusion. Specifically, spatial and channel attention mechanisms, deformable convolutions and cross-attention are used to extract, align and combine features from LDR images. The effectiveness of the proposed model was demonstrated through qualitative and quantitative evaluations on the Kalantari and Tel HDR datasets, and the results were compared other state-of-the-art methods. The results demonstrated that the model effectively preserved spatial consistency in the reconstructed images, achieving superior performance on the Kalantari dataset and competitive results on the Tel dataset.
Towards Zero-Shot Document Image Classification
Classification is a fundamental tool to automate the process of categorizing documents in many real-world applications, such as information management, financial document processing, healthcare records management, news categorization, fraud detection, regulatory compliance, and many others. Because of this broad spectrum of applications, document classification is of paramount importance for various companies. However, documents often change in terms of format and their visual patterns, which may impair a simple classification model. Moreover, model continuance and retraining often demands important efforts, consuming computational resources and demanding new data. Therefore, techniques capable of classifying documents by simply observing new data, without necessarily requiring retraining the classifier, are of immense importance for a wide variety of applications. In this context, Zero-Shot Learning (ZSL) is especially suitable for document classification because it handles diverse and ever-changing document content. In this work, we tackle the gap involving Zero-Shot Document Image Classification (ZS-DIC), where we classify documents that have not been seen by the model during training. To achieve this, we built Layout-Aware Complex Document Information Processing (LA-CDIP), a dataset tailored for this problem. LAC-DIP prioritizes structural consistency, allowing models to classify documents correctly under a ZSL scenario. To benchmark this dataset, we developed a series of Siamese Neural Networks (NNs) based on a variety of computer vision neural architectures, such as ResNet, EfficientNet, ViT and others. As a result, the proposed ZSL-based method achieves Equal Error Rates (EERs) under 5%. The code of the proposed method is available at https://github.com/ABMHub/doc-zsl.
Small Object Detection in Drone Captured Images for Maritime Surveillance
Maritime surveillance requires timely and accurate information processing to support critical decision-making, particularly in search and rescue (SAR) operations where lives depend on swift action. This paper addresses challenges in automated maritime object detection by introducing both a novel dataset and a model-agnostic framework. We present a novel dataset, Aussys, collected using unmanned aerial vehicles (UAVs) along the Brazilian coastline, specifically designed for maritime object detection in SAR contexts. Concurrently, we introduce a model-agnostic framework for post-flight analysis on small object detection in drone-captured maritime images, leveraging image slicing and advanced inference techniques. We evaluate the performance of three state-of-the-art object detection frameworks, YOLOv5, YOLOv8, and Detectronv2, on this new dataset and assess how their capabilities are enhanced through integration with Slicing Aided Hyper Inference (SAHI) methodology. Our dataset captures the challenging real-world conditions of maritime environments, including variable object scales, poor target-water contrast, and disruptive factors such as glare and wave patterns. Results demonstrate that while all models provide acceptable baseline performance, SAHI implementation significantly improves detection accuracy for small and distant objects critical targets in maritime SAR scenarios. This research advances Geospatial Intelligence capabilities by providing both specialized training resources and comparative analysis of enhanced detection frameworks, with direct applications to SAR operations where improved detection rates could substantially increase survival outcomes.
Hierarchical Vision Transformer Using Shifted Windows for Skeleton-Based Action Recognition
Skeleton-based human action recognition has gained significant attention due to the increasing accessibility of skeleton data. In this work, we propose a method for skeleton-based action recognition that leverages a hierarchical vision transformer with shifted windows, known as the Swin Transformer, combined with a self-supervised learning strategy inspired by Simple Masked Image Modeling (SimMIM). The Swin Transformer restricts selfattention to local windows and enables cross-window information exchange through a shift mechanism. This design ensures scalability with longer skeleton sequences while maintaining a balance between local and global context modeling and reducing feature redundancy. Given the limited availability of labeled data, especially for training large transformer-based models, we incorporate a self-supervised pre-training stage. This pretraining follows the SimMIM strategy, where masked patches of raw skeleton sequences are reconstructed using a one-layer linear head and L1 loss. This encourages the model to capture meaningful motion patterns even with partially visible data. Our method demonstrates competitive performance on public available datasets, including NTU RGB+D 60 and NTU RGB+D 120.
Unified-Copycat: Unifying Heterogeneous Classifiers Using Non Problem Domain Data
With the success of Convolutional Neural Networks, companies are increasingly offering trained models as paid services through APIs, where users are charged to query different models, without any access to their architecture or training datasets. Additionally, privacy regulations and ethical concerns have also led to the use of black-box models. These constraints make datasets and detailed information about the classifiers entirely unavailable to users. The challenge is further exacerbated when unifying models designed for tasks with different class sets, as class disparities significantly complicate the unification process. Given this scenario, unifying such specialized models into a single classifier provides clear advantages, but existing solutions often assume access to domain data or internal model structures. To address this, we propose a novel approach based on the Copycat framework that unifies heterogeneous models into one unified model capable of inference across all class domains. Unlike prior methods, our approach leverages random natural images from outside the domains of the heterogeneous models, reducing the assumptions required for unification. Comprehensive evaluations show that our method achieves competitive performance with significantly fewer assumptions, marking a step forward in unifying heterogeneous classifiers.
Flowcheck: A Discrete Flow Matching Approach for Generating Chess Configurations
We introduce FlowCheck, a novel method for generating realistic chess positions by treating the board as a discrete grid. Our approach uses Discrete Flow Matching (DFM) to train a Vision-Transformer-based model that generates board states conditioned on high-level strategic labels, such as material balance and engine score. A key innovation is the ability to create positions based on specific tactical concepts and a post-processing heuristic to refine the outputs for greater accuracy. FlowCheck produces configurations with a repetition rate of less than 0.02%, and our refinement step significantly improves the fidelity to the desired engine evaluation. This approach offers a powerful tool for procedural content generation, with applications in tailored player training, strategic exploration, and the creation of unique educational content.
A Study of Synthetic and Cross-Domain Real-Data Pre-Training for Aerial Farm Monitoring
The use of Unmanned Aerial Vehicles (UAVs) for monitoring farms has increased in recent years. Consequently, it is essential to enhance Artificial Intelligence capabilities to leverage the imagery captured by these devices fully. Training semantic segmentation models requires a large number of labeled images, which are nonexistent in the agricultural context. To address the bottleneck of manual labeling, we investigate the use of different pre-training strategies utilizing synthetic data from the same domain (Synthetic Dataset), e.g., UAV flights over rural areas in a virtual environment, and real data from a slightly different domain (Cross-Domain Dataset), e.g., high-resolution remote sensing images. Both quantitative and qualitative results demonstrated that pre-training using the Synthetic Dataset performed better in the final training, leading to an increase of 3.1 p.p. in IoU, 6.4 in F1-Score, and 7.5 in Recall when compared to the Cross-Domain pre-training strategy.
Enhancing Distilled Datasets Via Natural Data Mixing
Dataset distillation emerges as a promising technique to reduce web-scale datasets into a compact version with only a few samples per class. It involves distilling a large dataset into a compact synthetic set that aims to preserve representative information from the original data, offering advantages such as higher training efficiency and data privacy. However, existing techniques fail to fully capture the underlying properties of original (natural) training samples. Hence, learning solely on distilled images-the standard practice-leads models to encounter a notable generalization gap. In this work, we propose a simple yet effective mechanism to enhance distilled images. Our method transfers powerful and discriminative characteristics from natural images to distilled samples through a simple mixing process. Extensive experiments on benchmarks confirm that our method consistently improves generalization accuracy. Notably, we demonstrate that our approach enables distilled sets with only 10 images per class to match or exceed the performance of state-of-the-art methods trained on 50 images per class, representing a 5× gain in training efficiency. On challenging ImageNet subsets, it increases predictive performance by up to 11.5 percentage points. We also confirm that our method more effectively preserves internal representations concerning full dataset training when compared to plain state-of-the-art dataset distillation methods. Crucially, our method achieves these improvements without increasing the size of the distilled set, thus preserving the efficiency and privacy advantages inherent to dataset distillation. Moreover, our method enhances robustness to common corruptions, improving predictive performance by an average of 9.05 percentage points. It also improves accuracy against moderate adversarial attacks. Code is available at: github.com/IanPons/Enhancing-Distilled-Datasets
PD-Loss: Proxy-Decidability for Efficient Metric Learning
Deep Metric Learning (DML) aims to learn embedding functions that map semantically similar inputs to proximate points in a metric space while separating dissimilar ones. Existing methods, such as pairwise losses, are hindered by complex sampling requirements and slow convergence. In contrast, proxy-based losses, despite their improved scalability, often fail to optimize global distribution properties. The Decidability-based Loss (D-Loss) addresses this by targeting the decidability index (d′) to enhance distribution separability, but its reliance on large mini-batches imposes significant computational constraints. We introduce Proxy-Decidability Loss (PD-Loss), a novel objective that integrates learnable proxies with the statistical framework of d′ to optimize embedding spaces efficiently. By estimating genuine and impostor distributions through proxies, PD-Loss combines the computational efficiency of proxy-based methods with the principled separability of D-Loss, offering a scalable approach to distribution-aware DML. Experiments across various tasks, including fine-grained classification and face verification, demonstrate that PD-Loss achieves performance comparable to that of state-of-the-art methods while introducing a new perspective on embedding optimization, with potential for broader applications.
A Multi-Scale Diffusion Method for Chromatic Adaptation and Halo Artifact Suppression
Color blindness affects a significant portion of the population, it impacts the visual experience of affected individuals, making chromatic adaptation in digital content crucial for visual accessibility. Existing chromatic adaptation methods effectively improve visual perception but suffer from high computational costs and can produce unwanted halo artifacts. This paper proposes an efficient chromatic adaptation algorithm based on a multi-scale Gaussian pyramid strategy combined with local anisotropic diffusion, effectively eliminating halo artifacts while preserving image quality. Experimental results evaluated with PSNR, local SSIM, MSE, CIEDE2000 and Absolute Difference Maps demonstrate that the proposed method achieves visual quality comparable to state-of-the-art algorithms while drastically reducing processing times by over 90%. Thus, the proposed approach provides an efficient solution, enabling practical integration into iterative applications to enhance visual accessibility for colorblind users.
Diversity-Informed Fusion for Sclerotinia Sclerotiorum and White Mold Detection
The white mold caused by the soilborne pathogen Sclerotinia sclerotiorum affects hundreds of plant hosts around the world. Early detection is important to leverage effective and timely responses that mitigate crop yield losses. This paper addresses the automatic detection of white mold based on computer vision methods. We introduce a Diversity-driven Object Detection Fusion framework, DiODeFusion, that takes advantages of complementary views provided by multiple detectors to improve prediction results. DiODeFusion relies on bounding-box-based diversity measures to determine the most promising detectors for use in fusion. Experiments were conducted on a recently created SWM dataset, considering the assessment of multiple diversity measures, detector selection approaches, and bounding-box fusion strategies. Experimental results show that DiODeFusion achieves gains of up to 2.0% in terms of the F1 metric using only three detectors, compared to strong fusion baselines that employ all available detectors.
VideoFakeD: Video-Based Deepfake Detection
This paper presents VideoFakeD, a lightweight deepfake detection pipeline that challenges the growing complexity of current state-of-the-art methods. Our approach combines a diverse set of handcrafted features, covering texture, illumination, and spectral descriptors, with a compact ViViT-based architecture capable of modeling spatial and temporal inconsistencies. Classical descriptors are used to reveal subtle manipulation artifacts, enabling our model to operate effectively with significantly fewer parameters. Despite using only 554 K to 1.6 M parameters, VideoFakeD achieves competitive results on FaceForensics++ (96.71% ACC, 97.57 AUC), and generalizes well to face antispoofing tasks (6.38 HTER, 95.62 AUC on Replay-Attack). These results highlight VideoFakeD as a highly efficient and practical solution for deepfake detection, especially suited for real-world, resource-constrained environments.
Controllable Low-Light Image Enhancement with Restoration Level Estimator
Low-light image enhancement (LLIE) is an important task in image processing and computer vision, addressing the need to improve the visual quality of images captured in suboptimal lighting conditions. Enhanced images are not only more visually appealing but also more effective for downstream tasks such as object detection and classification. However, while the desired level of enhancement is often subjective and varies across users and applications, previous LLIE techniques do not allow users to control the desired level of enhancement intensity. We introduce the Restoration Level Estimator (RLE) block, a novel component designed to provide control over the enhancement level in existing LLIE models. The RLE block can be seamlessly integrated into convolutional neural networks, adding a new channel that allows users to adjust the level of enhancement applied to input images. Our experiments show that, in addition to offering control, the RLE block can improve the overall performance of LLIE models as measured by PSNR, SSIM, and LPIPS. We demonstrate the flexibility of our approach across multiple LLIE models, highlighting its potential to improve both user experience and model performance.
Mobile Solution for Alkalinity Estimation from Colorimetric Analysis via CNNs
We present a computer vision-based solution for estimating alkalinity from images of water samples. Applications include verification of product conformity, industrial process monitoring, and parameter assessment. The method is computationally efficient and implemented as a mobile app. A smartphone camera captures images under controlled conditions using a handheld 3D-printed chamber. Alkalinity is estimated by an artificial neural network using the chrominance distribution of the analyte image, rather than raw RGB data. The approach enables mobility and use outside controlled laboratories. It is suitable as a semi-quantitative analysis method, particularly in critical industries such as oil and gas.
Visualizing Communities in Dynamic Multivariate Networks
A dynamic (or temporal) network is a widely used structure that enables understanding dynamic systems by modeling interactions among system components over time. In many real-world cases, however, components (called nodes) and/or interactions (called edges) contain numerous meaningful attributes, leading to the need for a more suitable instrument for representing and analyzing these dynamic and complex systems with multiple attributes: the Dynamic Multivariate Network (DMVN). In this work, we extended LargeNetVis, a visualization system specifically designed for large dynamic networks that focus on network community structure and dynamics, to enable the visual exploration of DMVNs and their communities. The newly introduced visual encodings and interactions allow the visualization of nodes' and edges' attributes at different granularity levels and produce a node tracking capability from both top-down and bottom-up perspectives. With these functionalities, one can track individual nodes across dynamic communities over time. The proposed approach is validated by comparing it with the original LargeNetVis system and conducting a user evaluation involving 37 participants.
A Comparative Study on Few-Shot Learning for Retinal Disease Classification
The scarcity of annotated data in medical domains poses a major challenge for training deep learning models. Motivated by this, we evaluated 14 Few-Shot Learning (FSL) methods for retinal disease classification on BRSET, a Brazilian ophthalmology dataset, encompassing both the CNN and Vision Transformer backbones. We further assessed explainability using Grad-CAM to ensure that model decisions are aligned with pathological features. Our results highlight the potential of meta-learning for retinal image analysis, demonstrating that few-shot methods can achieve robust performance with minimal annotated data while maintaining interpretability through alignment with clinically relevant features. To support reproducibility and future research, we present Simple FewShot, an open-source library. The library is available at github.com/victor-nasc/SimpleFewShot.
A Deep Metric Learning Approach for Content Based Image Retrieval in Rock Tomography: A Triplet Loss Study
The automatic analysis of images in rock tomography datasets still is an unexplored task despite its high relevance in geological studies and characterization of materials. Traditional Content-Based Image Retrieval (CBIR) methods face difficulties when dealing with the structural complexity of these images while approaches based on deep learning still lack research specifically at this domain. In this paper we propose a CBIR approach using Deep Metric Learning with Triplet Loss function and different convolutional backbones. The results on the sandstone samples dataset has shown superior performance than the method found in the literature. The highlight was the DenseNet121 architecture, which obtained 99.23%±0.14 of F1-Score and 99.09%±0.16 of mAP@10. These results show the potential of the proposed approach to efficiently structure the similarity space in rock tomography images, demonstrating the relevance of the choice of a deep encoder when retrieving tomographic images in geological contexts.
Quo Vadis Pathology? Advancing Glomerular Lesion Classification with Foundation Models
Computational pathology is undergoing a significant transformation with the emergence of foundation models (FMs). These models leverage self-supervised learning on extensive histopathological datasets with the aim of extracting robust feature representations. FMs hold potential to automate advanced diagnostic pipelines, encompassing segmentation, classification, and biomarker discovery. This study evaluates the effectiveness of embeddings from four SOTA FMs (UNI, UNI2, Phikon, and Phikon2) for one-versus-all glomerular lesion classification. We propose here a comparative framework in which a multi-layer perceptron (MLP) and a support vector machine (SVM) - each trained exclusively on FM-derived embeddings - are benchmarked against EfficientNet, a fully supervised end-to-end image classifier. By varying the number of cross-validation folds (from k=2, representing minimal training data, to k=5, representing maximal training data), on a proprietary histopathology dataset, we assess classifier robustness under differing data regimes. Our results demonstrate that, even without any FM fine-tuning, the UNI/SVM pipeline outperforms the EfficientNet by 3.4 percentage points in average F 1-score, considering all values of k.
When Text and Image Meet Face Emotion Retrieval: Benchmark and a Variegated Dataset
Automatic facial emotion recognition, despite deep learning advancements, faces challenges with large manually annotated datasets and demographic imbalance. While visual-language models (VLMs) show promise in aligning visual and textual emotion data, content-based facial emotion retrieval is underexplored, and larger models exhibit demographic sensitivity. This study introduces Facial INtensity of Emotions (FINE), the first large-scale dataset balancing gender, race, and Ekman's six emotions, across four intensity of emotion levels. We propose a retrieval protocol and baseline using fine-tuned contrastive language-image pre-training-based VLMs, demonstrating that fine-tuning on FINE consistently improves accuracy and reduces performance variance across demographics and emotion classes, thereby mitigating representation bias. However, misclassification rates still increase at the highest intensity levels even after fine-tuning, indicating expression magnitude remains an open challenge. This work confirms fine-tuning's value in enhancing generalization and reducing variability in emotion retrieval, establishing FINE as a robust benchmark.
Density-Guided Rank Correlation Graphs for Graph Convolutional Networks in Image Classification
Graph Convolutional Networks (GCNs) have shown promising results in semi-supervised learning tasks, yet their effectiveness is highly dependent on the quality of the input graph. In image classification scenarios, graph construction remains a challenging step due to the general lack of inherent structural relationships between images. In this work, we propose a novel method to build input graphs for GCNs based on rank correlation measures between image similarity rankings. Each image is represented as a node, and edges are established according to the correlation between its ranked list of neighbors and those of other images. We introduce a density-guided strategy for automatically selecting the correlation threshold that controls the sparsity of the graph. Experiments conducted on three image classification datasets using three feature extractors and three GCN architectures show that the proposed correlation-based graphs outperform standard kNN and reciprocal kNN graphs in most cases, especially when used with the Simplified Graph Convolution (GCN-SGC) model. Our method surpasses several traditional and recent baselines, including techniques based on manifold learning and label propagation, while relying solely on contextual similarity through rank correlation without any post-processing refinement. The proposed approach's source code and documentation are publicly available at corgcn.lucasvalem.com.
Exploring Light-Weight Object Recognition for Real-Time Document Detection
Object Recognition and Document Skew Estimation have come a long way in terms of performance and efficiency. New models follow one of two directions: improving performance using larger models, and improving efficiency using smaller models. However, real-time document detection and rectification is a niche that is largely unexplored by the literature, yet it remains a vital step for automatic information retrieval from visual documents. In this work, we strive towards an efficient document detection pipeline that is satisfactory in terms of Optical Character Recognition (OCR) retrieval and faster than other available solutions. We adapt IWPOD-Net, a license plate detection network, and train it for detection on NBID, a synthetic ID card dataset. We experiment with data augmentation and cross-dataset validation with MIDV (another synthetic ID and passport document dataset) to find the optimal scenario for the model. Other methods from both the Object Recognition and Skew Estimation state-of-the-art are evaluated for comparison with our approach. We use each method to detect and rectify the document, which is then read by an OCR system. The OCR output is then evaluated using a novel OCR quality metric based on the Levenshtein distance. Since the end goal is to improve automatic information retrieval, we use the overall OCR quality as a performance metric. We observe that with a promising model, document rectification does not have to be perfect to attain state-of-the-art performance scores. We show that our model is smaller and more efficient than current state-of-the-art solutions while retaining a competitive OCR quality metric. All code is available at https://github.com/BOVIFOCR/iwpod-doc-corners.git
Deep Learning for Protoplanetary Disk Classification from Hubble Images: A Comparative Study on Imbalanced Data
Protoplanetary disks (PPDs) are structures composed of gas and dust surrounding young stars, which play a fundamental role in understanding the formation of planetary systems. Despite their significance in astronomy, these objects remain underexplored in the image recognition literature. This study presents a comparative analysis of deep learning architectures for PPD classification, based on a catalog curated by domain experts. The dataset includes images captured by the Wide Field Channel of the Advanced Camera for Surveys (ACS/WFC) from the Hubble Space Telescope (HST) as part of the Orion Nebula Treasury program, along with annotations from the consolidated PPD catalog. To address class imbalance we rearrange the original classes and use data augmentation techniques of geometric image transformations and band combination. The experimental setup explores a range of neural network architectures and hyperparameters, including traditional models, such as VGG, ResNet, Inception and EfficientNet, as well as a more recent ConvNeXt architecture. An ablation study is also conducted to assess the contribution of NLM+Anscombe as a denoising step. The results indicate a general difficulty in learning discriminative patterns from the data. Among the evaluated architectures, EfficientNet achieved the best overall performance, with a mean F1-score of 53%, reaching a maximum of 65% using a learning rate of 0.02 and a weight decay of 0.0004. The denoising filtering showed no statistically significant impact on model performance for the specific configuration.
Memory-Augmented Long Short-Term Memory for Dynamic Video Summarization
Capturing relevant content from videos while preserving temporal coherence remains a central challenge in video skimming. The prevalence of redundant information often hinders the extraction of meaningful content, especially when the goal is to retain the central narrative of the video. While scene change detection can aid in segmenting video content, conventional methods often struggle with highly diverse and repetitive scenes due to their limited ability to model temporal dependencies and detect transitions effectively. To address these limitations, we propose the Memory-Augmented LSTM for Dynamic Video Summarization - MALSumm, a supervised architecture based on Extended Long Short-Term Memory (xLSTM) networks that enhances memory capacity through a dual-path design. This design integrates weighted memory to evaluate local and global information, allowing the model to preserve fine-grained details while maintaining overall temporal consistency, all within a low-complexity framework. Experimental Results validate the effectiveness of our approach, achieving an average F-score of 49.7 on the SumMe dataset and 62.1 on TVSum, outperforming recent supervised baselines. Additionally, when measuring alignment with human annotations, the model attains a Kendall's τ of 0.180 and Spearman's ρ of 0.242, exceeding the scores reported for human agreement. These findings demonstrate that our method provides a competitive and lightweight solution for dynamic video summarization, effectively balancing accuracy and efficiency.
Semi-Supervised Image Retrieval Through Particle Competition and Cooperation Combined with Manifold Learning
The growing volume of unstructured visual data poses significant challenges for content-based image retrieval, particularly in scenarios where labeled information is scarce. This motivates the development of semi-supervised strategies capable of enhancing retrieval performance using limited supervision. In this work, we propose a novel approach that combines manifold learning and class-based probabilistic modeling for semi-supervised image retrieval. The method employs the Particle Competition and Cooperation (PCC) model to generate class dominance vectors using only a small portion of labeled data. These vectors are then fused with low-dimensional embeddings derived from Uniform Manifold Approximation and Projection (UMAP), yielding a representation that preserves neighborhood structure while capturing semantic class distributions. Experiments on three image datasets—Oxford 17 Flowers, Corel5k, and CUB-200—demonstrate the effectiveness of the proposed approach, which consistently outperforms baseline descriptors and isolated enhancements.
Automated Synthetic Data for Computer Vision: Blender-COCO Pipeline Enhancing Yolov8
This paper introduces an automated and reproducible pipeline for the generation of synthetic datasets with COCO-format annotations, designed to support object detection and segmentation tasks. The proposed framework combines controlled video acquisition of real-world objects, volumetric reconstruction via the SVRaster algorithm, automated mesh postprocessing in Blender, and synthetic rendering with systematically varied lighting and backgrounds. Annotation generation is fully automated through the custom YOLO FOTO plugin, which orchestrates virtual camera placement and metadata export. The resulting synthetic images are employed to train the YOLOv8n model. The study is conducted under the Design Science Research (DSR) methodology, ensuring methodological rigor and reproducibility. Experimental results demonstrate substantial improvements in model performance when synthetic data are incorporated into training, underscoring their effectiveness in enhancing dataset diversity and robustness. These findings highlight the strategic value of synthetic data in scenarios where manual annotation is costly, time-consuming, or otherwise constrained.
Fortifying Reversible Face Anonymization: A Secure Image Embedding Loss to Prevent Identity Leakage
With the proliferation of image capture devices and social media, face recognition technology has become widespread, raising significant privacy concerns and leading to the development of data protection regulations like GDPR and LGPD. Reversible face anonymization techniques, which aim to obscure an individual's identity while allowing for its recovery with a secret key, have emerged as a promising solution to balance privacy and data utility. These methods, often based on Generative Adversarial Networks (GANs), generate realistic, anonymized faces that preserve the original image's structure. This paper challenges the presumed security of state-of-the-art reversible face anonymization systems. We introduce a novel attack that bypasses the password-based protection by exploiting information leaked in an intermediate stage of the anonymization pipeline. Our attack on a leading reversible anonymization method, RiDDLE, successfully recovers the original identity from anonymized images in up to 70.2% of cases on the VggFace2 dataset, a stark contrast to the 0.2% chance of a random guess. To counteract this critical vulnerability, we propose a new loss function, the Secure Image Embedding Loss, designed to be integrated into the training of anonymization models. This addition enforces the disassociation of the original identity from the intermediate representations. Experimental results demonstrate the effectiveness of our defense, reducing the success rate of our attack to a mere 0.8% on VggFace2 and showing robust performance on the LFW dataset. These findings highlight a significant, previously overlooked security flaw in reversible face anonymization and provide a concrete solution to fortify these methods against such attacks, marking a crucial step toward ensuring genuine privacy in facial data.
Longitudinal White Matter Hyperintensity Segmentation with LSTM-Enhanced U-Net
White matter hyperintensities (WMHs) are commonly accepted biomarkers of brain aging and neurodegeneration, typically observed on magnetic resonance imaging. Segmenting WMHs using U-Net-based convolutional neural networks (CNNs) is a viable and widely demonstrated technique to automate an otherwise laborious and tedious manual task. Although automation of cross-sectional WMH segmentation has been demonstrated, failure to appropriately process temporal information may decrease accuracy and limit understanding of lesion progression. Here, we investigate the combination of two convolutional long short-term memory (LSTM) approaches and U-Net models to segment WMHs in young and old adults. Using information acquired at two visits in 50 healthy individuals, we analyzed model-task performance using F-measure of three variants: 1) baseline standard U-Net, 2) Partial LSTM U-Net, and 3) Full LSTM U-Net. Results showed that while LSTM-based models outperform the baseline model in F-measure, notable differences emerged in how effectively they detected WMHs of varying size and distribution between age groups. These findings highlight the need to account for age-related heterogeneity in lesion morphology when designing and evaluating automated longitudinal WMH segmentation tools, and suggest that age- and size-aware modeling may lead to more robust predictions.
An Iterative Approach to Determine the Lower Bound of Depth Resolution in Stereo Vision Systems
We present a novel method to calculate the lowest expected depth resolution for non-parallel and heterogeneous stereo vision systems automatically based on a simulated environment. Our algorithm iterates over an arbitrary 3D counterpart to an epipolar line and uses a modified binary search to find the depths in which the disparity changes. We show how, given the parameters of the stereo system, our method can calculate what is the best resolution one algorithm might achieve if they do not use sub-pixel information. Throughout this paper, we show how changes in the scene might affect the resulting lower bound, such as increasing camera resolution or changing camera angles around the subject. Furthermore, we propose a method to compare and plot stereo vision algorithms against the generated lower bound. We anticipate that our approach may reduce development costs for stereo vision solutions by minimizing the necessity to physically construct stereo systems prior to assessing their potential accuracy.
Toward Linear Representations of Foundation Models for Histopathology Image Retrieval
While domain-specific foundation models have accelerated advancements by leveraging large-scale histopathological datasets, they often struggle to generalize across diverse clinical scenarios and datasets. In this paper, we address this limitation with a linear-prototype framework: A 128-D projection head trained by a modified prototypical loss on a few labelled slides. Evaluated on proprietary glomerular biopsies, dermoscopic skin cancer, and ovarian-cancer WSIs, our proposal approach consistently boosts image retrieval. Across nine backbones (UNI/UNI2h, Phikon/Phikon-v2, Virchow2, DINO/DINOv2, ViT, and a ResNet-50 baseline), few-shot tuning raises mAP by up to 15 percentage points. These gains show that our lightweight, prototype-based layer can reconcile the breadth of foundation pre-training with the depth of task-specific discrimination, enabling improvement on medical-image retrieval across diverse pathologies.
Real-Time Monitoring and Historical Analysis of Rainfall and its Impacts on Urban Areas
Climate change, combined with unplanned urban growth, has intensified the occurrence of natural disasters in developing countries like Brazil, leading to severe floods and significant loss of life. Recent events, such as the 2024 floods affecting 90% of the cities in Rio Grande do Sul, highlight the urgency of improving disaster response. Governments and citizens increasingly share large volumes of disaster-related data, offering opportunities for better mitigation strategies. This paper presents PluvWeb, a visual system that integrates heterogeneous data sources to support the analysis of rainfall impacts in urban areas. The system automates landslide response routes, detects floods from traffic cameras, and analyzes rainfall patterns, with effectiveness shown in case studies from a Brazilian city.
Dynamic Model Weighting via Stacking-Meta Learning for Multi-Class Classification
Ensemble learning has emerged as a powerful solution for addressing the challenges associated with complex, high-dimensional, and imbalanced datasets across structured and unstructured domains. However, traditional ensemble approaches often lack mechanisms for dynamically selecting and weighting models based on their predictive strengths and uncertainties, thus limiting their performance in multi-class classification tasks. To overcome this limitation, we propose a novel Stacking-Meta Learning approach, which transfers the model selection and weighting responsibilities to an adaptive outer meta-model. This meta-model dynamically identifies and leverages statistical patterns within the probabilistic outputs of diverse inner models. Our approach significantly enhances predictive accuracy and robustness by adaptively balancing precision and recall across changing data patterns. We clarify the design of our dynamic scoring function and its properties (boundedness, monotonicity, and convex combination). Extensive experimental evaluation on two representative datasets shows that the Stacking-Meta Learning method achieves competitive or superior performance against strong single models and a bagging ensemble, in both structured and unstructured multi-class settings. In low-data regimes, it maintains robust performance. We discuss computational considerations and outline ablations and additional baselines as directions for future work.
Quantitative Analysis of Visual XAI for Multiclass DeepFake Detection Based on CNN
The proliferation of deepfakes, highly realistic synthetic media generated through techniques such as Generative Adversarial Networks (GANs) and Diffusion Models, has introduced new challenges to digital media integrity and public trust. These manipulations are often indistinguishable to the human eye and pose significant threats, including misinformation, identity fraud, and the erosion of credibility in audiovisual content. Convolutional Neural Networks (CNNs) have demonstrated strong performance in detecting deepfakes by capturing subtle generation artifacts. However, their opaque decision-making processes raise concerns about interpretability and trustworthiness, particularly in sensitive domains such as media forensics and biometric authentication. This study conducts a quantitative analysis of visual explainable AI (XAI) techniques applied to CNN-based classifiers. Our approach employs a quantitative framework to evaluate the outputs produced by various visual XAI methods, including Grad-CAM, SmoothGrad, IntegratedGradients, InputXGradient, and Saliency map. The evaluation is conducted across multiple dimensions: Saliency Entropy, Salience-assessed Reaction to Noise, Salience Resilience to Geometrical Transformations, and Accuracy over SLIC-based Segmentation. Experiments are conducted on the FaceForensics++ dataset, which includes multiple manipulation types (e.g., FaceShifter, DeepFake, Face2Face, FaceSwap, and NeuralTextures). The quantitative analysis explores whether patterns in saliency behavior differ significantly between authentic and synthetic samples and whether these metrics can serve as auxiliary indicators for deepfake detection. This work contributes to enhancing the interpretability, reliability, and adoption of deep learning models in forensic applications.
Deepfake Detection with GenConViT and DeepfakeBenchmark: A Comparative Study on the Deepspeak Dataset
Deepfake videos have evolved rapidly, exploiting Generative Adversarial Networks (GANs) and Transformer advances to produce persuasive face-swap and lip-sync forgeries. This paper revisits automatic detection through the Generative Convolutional Vision Transformer (GenConViT). After just five epochs of fine-tuning on the recent DeepSpeak challenge, 44 h of video spanning 220 identities, GenConViT-AE achieves an area under the Receiver Operating Characteristic (ROC) curve of 0.993 [0.990, 0.996] and 93.82% [92.53, 95.04] accuracy, outperforming the best baseline by approximately one percentage point in Area Under the Curve (AUC). The detector also identifies 98.8% of forged videos while maintaining an 88.9% true-negative rate, confirming balanced sensitivity and specificity. Ablation studies reveal diminishing returns beyond five epochs, and error analysis highlights persistent challenges of falsification techniques. These results position domain-tuned hybrid CNN-Transformer architectures as robust solutions for large-scale forensic pipelines.
Detection and Segmentation of Dental Features from Tomographic Images for Age Estimation
The age estimation represents a critical component in the forensic identification of individuals and decomposed remains. Dental analysis offers exceptional potential for age determination because teeth are the most durable structures in the human body and exhibit predictable age-related morphological changes. This study presents a comprehensive computer vision approach that uses deep learning models to detect and segment teeth in Cone Beam Computed Tomography images, extracting geometric and deep feature representations for accurate age prediction. Our approach is evaluated on a challenging dataset composed exclusively of adult subjects (ages 18-60), which are typically harder to estimate. Despite this, the method achieves low Mean Absolute Error values, demonstrating competitive performance compared to the existing literature.
A U-Net-Based Approach for Histological Tissue Segmentation Using RCAug Data Augmentation
Histopathological analysis of tissue samples remains the gold standard for cancer diagnosis; however, the process is time-consuming and subject to inter- and intra-observer variability. Computer-aided diagnosis systems are crucial for mitigating these issues by automating key tasks, such as the segmentation of clinically relevant structures. However, the accuracy of these segmentation methods is often limited by data scarcity and morphological variability. To address these challenges, this study proposes a previously unexplored association of enhanced U-Net architectures with RCAug, a composite, structure-preserving data augmentation strategy. The study evaluated enhanced U-Net architectures across four public datasets, comparing the proposed method against conventional augmentation techniques and a baseline without augmentation. The results demonstrate that the RCAug-based approach is a consistent top-performer, statistically outperforming other strategies. Notably, our proposed approach achieves competitive performance, reaching Dice scores of 90.19% on the GlaS dataset and 91.78% on the OCDC dataset. Our results indicate that a sophisticated augmentation strategy is a critical and efficient pathway to high performance in histological segmentation, enabling standard architectures to achieve competitive results without requiring more complex models or extensive pre-training.
ZEDD-G: ZEro-Shot Diffusion-Based Data Generation for Improving Classification Tasks
Generating synthetic data with diffusion models poses a challenge due to the scarcity of data in computer vision. However, its practical application presents challenges, including the need for computationally expensive retraining and the complex, manual effort required to design precise textual instructions. This work approaches these limitations by introducing the ZEDD-G method, a novel zero-shot and prompt-free framework for synthetic data augmentation specifically designed to enhance downstream classification tasks. Our methodology establishes a fully automated pipeline that begins with unsupervised clustering of visually similar images, followed by a multi-image latent guidance mechanism. This mechanism combines visual prompts with direct manipulation of latent features to generate diverse and controlled variations. Evaluated on a demanding classification benchmark, which includes natural and medical images, ZEDD-G demonstrates a substantial impact on performance. For a ResNet-50 model trained from scratch, our method boosts accuracy by an average of over 30 percentage points across diverse datasets. For a pre-trained model, it provides consistent gains of around 2.6 percentage points. These results are competitive with state-of-the-art fine-tuning-based methods, achieved without requiring any model retraining. Thus, ZEDD-G is an efficient method that enables the generation of high-quality synthetic data, thereby improving classification tasks. Our implementation is publicly available at https://github.com/Gardiy/ZEDDG.
Lightweight Hybrid Ensemble for Ameloblastoma and Ameloblastic Carcinoma Classification
The accurate diagnosis of rare odontogenic tumors, such as ameloblastoma and ameloblastic carcinoma, is essential for appropriate clinical management, but is complicated by their overlapping histopathological patterns. Whole-slide imaging is essential for capturing high-resolution histological details over large tissue areas; however, it generates thousands of image patches per slide, resulting in a massive volume of data that demands efficient and scalable classification models. This study proposes a Lightweight Hybrid Ensemble (LHE) that combines three CNN-Transformer architectures (EdgeNeXt, EfficientViT, and LeViT), optimized for a low parameter count and minimal computational cost (0.8GMACs,13.9M parameters). Evaluated across four magnification levels (5×,10×,20×, and 40×), the ensemble achieved its highest performance at 40× with an accuracy of 89.0% and an F1-score of 0.89, aligning with clinical diagnostic practices that rely on high-resolution details. Analysis across multiple magnifications and external validation on an independent dataset reinforce the method's robustness. These findings highlight the potential of lightweight hybrid ensembles for reliable and efficient histopathological classification, particularly in resource-constrained settings, filling a critical gap in the literature.
Exploring the Use of Synthetic Data to Improve Pedestrian Detection in CNN
In this paper, we investigate the use of synthetic data generation to enhance the accuracy of a pedestrian detection task utilizing a popular Deep Convolutional Neural Network framework (YOLOv10). Using virtual characters, we develop a model for creating computer-generated images, simulating these characters in a virtual environment, and automatically annotating the data based on their position. The synthetic data is then mixed with the real data to fine-tune YOLO and test its capacity to detect pedestrians. We experiment using MOT20 datasets, a benchmark for pedestrian detection, and create a combination of mixing real and CG data. Results show that fine-tuning with Computer graphics data can positively impact the neural network's Precision, Recall, and F1-score metrics, with promising potential in improving model performance and robustness, especially in scenarios with limited real data.
Interactive Exploration and Explanation of Spatio-Temporal Anomalies with Graph-LLM Integration
Understanding spatiotemporal anomalies is critical in domains such as urban safety, mobility, and environmental monitoring. These scenarios involve complex dynamics that are effectively modeled using graph-based representations, where the spatial structure is encoded through data connectivity, and each node corresponds to a time series. Anomaly detection in such data is crucial for identifying unusual or significant events, but it requires complex methods involving pattern recognition, prediction, and classification. Interpreting these anomalies remains challenging. To address this, we introduce an interactive system that combines spatiotemporal visualizations with Large Language Models (LLMs) to generate context-aware explanations by unifying temporal, spatial, and textual insights. We guide the LLM using a structured prompting strategy grounded in the data to reduce hallucinations and improve plausibility. As a demonstration of functionality, we analyze crime anomalies in São Paulo, uncovering links to events such as Carnival and religious holidays.
Adapting Synthetic Eyes: A Study of Pixel and Feature-Level UDA for Traffic Object Detection
Training robust object detectors for autonomous driving requires vast amounts of annotated data, making the use of synthetic datasets an attractive alternative. However, models trained on synthetic data suffer from a significant performance drop when deployed in the real world due to the “sim-to-real” domain gap. Unsupervised Domain Adaptation (UDA) aims to solve this problem without requiring expensive target domain annotations. This paper conducts an empirical study comparing two leading but philosophically different UDA paradigms: pixel-level adaptation via image-to-image translation (CycleGAN) and feature-level adaptation via confidence-aware data mixing (ConfMix). We evaluate these methods on challenging synthetic-to-real adaptation tasks, using DOLPHINS and SIM10K as source domains, and Cityscapes and nuScenes as target domains. Our findings demonstrate that the feature-level mixing strategy of ConfMix provides more significant and robust performance gains than pixel-level translation with CycleGAN. Furthermore, we introduce and evaluate a hybrid method, TransConfMix, which yields mixed results, highlighting the complexities of combining these techniques. Our work provides clear evidence and practical guidance on the effectiveness of different UDA strategies, concluding that directly adapting the model's learning process is a more potent approach than preprocessing the data for this critical application.
Graph Convolutional Networks Based on Aggregated Multilevel kNN Graph for Image Classification
Graph Convolutional Networks (GCNs) rely heavily on the quality of the input graph, which is often defined by a fixed neighborhood size. This work explores two complementary strategies to make GCN-based classification more robust. First, we build multiple graphs with different neighborhood sizes to capture diverse structural information. Second, we leverage the full softmax output distributions to apply confidence-based weighting and combine predictions through lightweight ensembles. This approach improves accuracy without modifying the GCN architecture or training process. Results show consistent gains across different models and graph configurations, highlighting the benefit of integrating multi-scale graph structures and uncertainty signals in the decision process.
Generating Urban Forest Datasets from Satellite Imagery: Application to the City of São Paulo
The connection between urban forests and phenomena such as heat islands, air pollution, property values, and public perceptions of safety has been the subject of extensive research over the years. A fundamental requirement for such studies is the accurate mapping of the spatial distribution of green areas. In this context, this work presents a concise and reproducible framework to map urban forests and generate datasets detailing their location and distribution within metropolitan regions. The proposed methodology relies on freely available imagery from the CBERS-4A satellite and the open-source QGIS software. While auxiliary layers such as city layout and shadow masks are used during the modeling process, their purpose is solely to enhance the accuracy of urban forest classification, not to serve as final outputs. The true merit of this approach lies in its ability to identify urban tree cover patterns with high accuracy, making it a scalable solution for urban green infrastructure studies. The framework is applied to map urban forests in the central area of the city of São Paulo, Brazil.
Circle-Based Object Counting Using Image Analysis
Accurate estimation of object counts in binary images with spatially clustered or overlapping instances remains a challenging problem in fields such as microscopy, microfluidics, and industrial inspection. Classical segmentation techniques, including contour-based and watershed algorithms, often perform suboptimally in scenarios characterized by occlusion, high object density, or morphological variability. In this work, we present an unsupervised, training-free method for object counting based on stochastic placement of fixed-radius circles within segmented regions, followed by iterative merging using geometric criteria. Experimental results on synthetic datasets with varying degrees of object overlap indicate that the proposed approach provides robust and reliable estimates, frequently outperforming standard baselines, especially in cases of moderate to high agglutination. Qualitative analysis further demonstrates its ability to resolve individual objects within complex clusters while maintaining computational efficiency. These findings highlight the practical potential of the method for unsupervised object counting in challenging imaging scenarios.
DualMatch: A Dual EMA Teacher for Underwater Semi-Supervised Pipeline Segmentation
Underwater pipeline semantic segmentation remains a challenging task due to visual distortions, limited labeled data, and complex class boundaries. In this work, DualMatch is proposed, a semi-supervised segmentation framework based on a Dual Teacher architecture with Exponential Moving Average (EMA) models. Unlike prior approaches that rely on a single teacher, our method leverages two complementary teacher networks to reduce noise and enhance consistency in generating pseudo-labels. The powerful DINOv2-Small is adopted as the encoder, and our model is evaluated on a challenging underwater dataset. To support this research, a dataset comprising images from Remotely Operated Vehicle (ROV) and Autonomous Underwater Vehicle (AUV) inspections is proposed, with semantic labels manually annotated for five object classes. Experimental results show that DualMatch achieves the best performance in four out of five semantic classes, and notably reaches an IoU of 84.69% in the Pipeline class, outperforming all compared methods, including UniMatchv2 and Allspark. Overall, the proposed approach sets a new standard in underwater semantic segmentation by delivering consistent and high-accuracy predictions across complex classes, most notably for pipeline segmentation. The code and dataset are available at: https://github.com/EduardoLawson1/DualMatch
Sonar Image Datasets: A Comprehensive Survey of Resources, Challenges, and Applications
Sonar images are relevant for advancing underwater exploration, autonomous navigation, and ecosystem monitoring. However, the progress depends on data availability. The scarcity of publicly available, well-annotated sonar image datasets creates a significant bottleneck for the development of robust machine learning models. This paper presents a comprehensive and concise review of the current landscape of sonar image datasets, seeking not only to catalog existing resources but also to contextualize them, identify gaps, and provide a clear roadmap, serving as a base guide for researchers of any kind who wish to start or advance in the field of underwater acoustic data analysis. We mapped publicly accessible datasets across various sonar modalities, including Side Scan Sonar (SSS), Forward-Looking Sonar (FLS), Synthetic Aperture Sonar (SAS), Multibeam Echo Sounder (MBES), and Dual-Frequency Identification Sonar (DIDSON). An analysis was conducted on applications such as classification, detection, segmentation, and 3D reconstruction. This work focuses on state-of-the-art advancements, incorporating newly released datasets. The findings are synthesized into a master table and a chronological timeline, offering a clear and accessible comparison of characteristics, sizes, and annotation details datasets.
A Curvature-Based Adaptive UMAP for Manifold Learning in Clustering and Classification
The Uniform Manifold Approximation and Projection (UMAP) algorithm is a powerful tool for dimensionality reduction, renowned for its ability to preserve both local and global data structures. However, its efficacy relies on a critical hyperparameter: the number of neighbors, which remains fixed for all data points. This rigidity can be suboptimal, as the intrinsic dimensionality and density can vary significantly across a manifold. In this paper, we introduce SH-UMAP, an extension that determines the neighborhood size for each point adaptively. SH-UMAP estimates the local curvature of the data manifold using a numerical approximation of the shape operator. Points in high-curvature regions are assigned smaller neighborhoods, while points in flatter regions are given larger ones. We present a preliminary experimental evaluation on benchmark datasets. The results indicate that the embeddings generated by SH-UMAP lead to improved performance in both unsupervised clustering and supervised classification tasks when compared to the standard UMAP algorithm, demonstrating the practical benefits of an adaptive-neighborhood approach.
Does Isotropy Improve 3D Deep Learning Segmentation? A Comparative Study on Cardiac MRI
Deep learning networks have excelled in several fields, achieving state-of-the-art quality in many complex problems. In the Medical Image Processing field, various three-dimensional (3D) deep learning methods have been proposed and validated for automatic segmentation. One of the main advantages of 3D strategies over simpler two-dimensional (2D) approaches is improved consistency by leveraging inter-slice contextual information. However, comparative studies show that 3D and 2D approaches achieve similar results in many segmentation tasks. A common property that affects mainly 3D strategies and is often ignored by many network designs is the data anisotropy. While very common in several medical imaging modalities, anisotropy is often disregarded by many studies, and its effect on 3D performance is still an underexplored topic. Considering this limitation, in this work, we investigate the advantages of promoting data isotropy on 3D deep learning segmentation. To do this, we propose a fully automated 3D segmentation pipeline that explores data standardization and slice interpolation methods to achieve adjustable levels of anisotropy. We performed cross-validation experiments considering two public cardiac MRI datasets for left ventricle segmentation, which is a popular problem containing highly anisotropic data. Then, we compare the performance of 2D and 3D U-nets trained on different anisotropy levels. The results indicate that promoting isotropy can significantly improve the performance and generalization ability of 3D deep learning approaches. However, the high computational cost, aligned with low improvements when compared to 2D strategies, may discourage its applicability in clinical practice.
Advancing Annotat3D with Harpia: A CUDA-Accelerated Library for Large-Scale Volumetric Data Segmentation
High-resolution volumetric imaging techniques, such as X-ray tomography and advanced microscopy, generate increasingly large datasets that challenge existing tools for efficient processing, segmentation, and interactive exploration. This work introduces new capabilities to Annotat3D through Harpia, a new CUDA-based processing library designed to support scalable, interactive segmentation workflows for large 3D datasets in highperformance computing (HPC) and remote-access environments. Harpia features strict memory control, native chunked execution, and a suite of GPU-accelerated filtering, annotation, and quantification tools, enabling reliable operation on datasets exceeding single-GPU memory capacity. Experimental results demonstrate significant improvements in processing speed, memory efficiency, and scalability compared to widely used frameworks such as NVIDIA cuCIM and scikit-image. The system's interactive, human-in-the-loop interface, combined with efficient GPU resource management, makes it particularly suitable for collaborative scientific imaging workflows in shared HPC infrastructures.
Performance Assessment of Optical and SAR Imagery for Superpixel-Based Deforestation Mapping in the ForestEyes Project
This study investigates the potential of SAR data (Cand L-band), individually and combined with optical imagery (Landsat-8 OLI), for classifying deforested areas within the ForestEyes project. Six classification scenarios with varying data combinations were defined. Images were segmented using the Simple Linear Iterative Clustering (SLIC) algorithm, and Haralick texture descriptors were extracted from each superpixel. Classifications were performed using Support Vector Machine (SVM) and Random Forest (RF), with ground truth labels derived from PRODES (Monitoring Program of the Amazon Forest by Satellite). Performance evaluation was based on 100 classification iterations, from which 95% confidence intervals were calculated for confusion matrix-derived metrics, including recall and precision. Results showed that scenarios using optical data yielded the best classification outcomes, with high recall and precision values for the Recent Deforestation class. The inclusion of SAR data did not lead to statistically significant improvements and, increased Recent Deforestation false positives, especially in scenarios relying exclusively on SAR data. Confidence interval analysis confirmed the superior performance of optical imagery for detecting deforested areas in this context.
BeSIM: A Benchmark for Evaluating the Interpretation of Social Interactions in Brazil Using Multimodal Large Language Models
The rapid development of Multimodal Large Language Models (MLLMs) has expanded the possibilities for automatically understanding complex real-world scenarios. However, their ability to interpret social interactions through videos remains underexplored. This study introduces BeSIM, a benchmark designed to evaluate MLLMs' competence in interpreting such interactions among Brazilians, based on the APRACE taxonomy for categorizing key elements of social interaction. A set of 22 videos was collected from YouTube, resulting in 110 multiple-choice questions aligned with these categories. The results show that models such as Gemini 2.5 Pro outperform their performance on generalist benchmarks like Video-MME, reaching up to 90 percent of accuracy on BeSIM. We also conducted a qualitative analysis to discuss possible features to explain failure/success of the models. These findings indicate that, when properly evaluated, MLLMs demonstrate great potential in interpreting human interactions. Code and data available at https://github.com/M4Falcao/BeSIM.
Visual Similarity Search of Cattle Brands Using Deep Learning on Binary Representations
Identifying cattle brands is a challenging visual task due to variability in branding styles, degradation over time, and the absence of standardized, large-scale datasets. To address this, we propose a deep learning-based Content-Based Image Retrieval (CBIR) framework specifically designed for this problem. Our system matches hand-drawn or digitized query sketches of cattle brands against a reference database of binarized brand symbols using learned visual embeddings for similarity search. To support training and rigorous evaluation, we assembled the Apporteira Cattle Brand Dataset, comprising 5,233 clean binary brand templates, 1,454 hand-drawn sketches, and over 627,000 augmented images simulating real-world distortions via rotations, morphological operations, and homographic transformations. This dataset has been made publicly available to enable reproducible research and benchmarking. We evaluate classical feature descriptors, pretrained convolutional neural networks (ResNet-50, MobileNet, VGG), and a fine-tuned VGG-16 model adapted to this domain. Experiments are reported with standard CBIR metrics, including mean Average Precision (mAP) and Top-$k$ accuracy, together with retrieval efficiency using Facebook AI Similarity Search (FAISS) to assess scalability. Our fine-tuned model achieves 79.71% Top-1 and 97.18% Top-10 accuracy, substantially outperforming generic CNN baselines and handcrafted methods. The results highlight the effectiveness of task-specific fine-tuning, showing consistent gains even when baselines perform strongly at higher ranks, and demonstrate the system's robustness to symbol variation, offering a scalable solution for livestock identification, rural security enforcement, and brand registry automation.
LPLC: A Dataset for License Plate Legibility Classification
Automatic License Plate Recognition (ALPR) faces a major challenge when dealing with illegible license plates (LPs). While reconstruction methods such as super-resolution (SR) have emerged, the core issue of recognizing these low-quality LPs remains unresolved. To optimize model performance and computational efficiency, image pre-processing should be applied selectively to cases that require enhanced legibility. To support research in this area, we introduce a novel dataset comprising 10,210 images of vehicles with 12,687 annotated LPs for legibility classification (the LPLC dataset). The images span a wide range of vehicle types, lighting conditions, and camera/image quality levels. We adopt a fine-grained annotation strategy that includes vehicle- and LP-level occlusions, four legibility categories (perfect, good, poor, and illegible), and character labels for three categories (excluding illegible LPs). As a benchmark, we propose a classification task using three image recognition networks to determine whether an LP image is good enough, requires superresolution, or is completely unrecoverable. The overall F1 score, below 80% for all three baseline models (ViT, ResNet, and YOLO), together with the analyses of SR and LP recognition methods, highlights the difficulty of the task and reinforces the need for further research. The proposed dataset is publicly available at https://github.com/lmlwojcik/lplc-dataset.
RuleText-AD: Logical Anomaly Detection via Textual Rule Engines Generated by MLLMs
Visual anomaly detection is an important tool in industrial quality control that may impact production efficiency and product quality. Traditional supervised methods struggle due to the difficulties and cost to obtain labeled anomaly data, particularly logical anomalies that violate global constraints that yet appear normal samples. This study investigates multimodal large language models (MLLMs) in few-shot prompting configurations to address this issue. We introduce RuleText-AD, a lightweight anomaly detection framework based on textual rule engines automatically generated by MLLMs from minimal visual examples and concise textual schemas. RuleText-AD eliminates the need for external segmentation tools, extensive prompt engineering, or heavy visual processing backbones, enabling efficient anomaly detection via high-level attribute reasoning. Experiments are conducted on the MVTec-LOCO logical anomaly benchmark, demonstrating that Gemini 2.5 Flash provides the best balance between accuracy and computational cost, achieving competitive performance with significantly reduced deployment complexity. This work highlights the potential for scalable, interpretable, and cost-effective anomaly detection solutions suitable for diverse industrial environments. Our method targets logical (global, ruleviolation) anomalies and does not address structural, pixel-level defects.
OCEG: an Interactive Visualization System for Classifier Ensemble Generation
Classification is a fundamental task in machine learning. However, individual models can struggle to deliver accurate results in various scenarios. Ensemble methods, which combine multiple classifiers, can enhance performance, but selecting and fine-tuning these models can be time-consuming. We introduce OCEG, a visual system designed to support the interactive exploration and composition of classifier ensembles. OCEG features a web-based interface that utilizes visual cues and pruning algorithms to highlight differences between models, automatically optimize ensemble parameters, and compare alternatives from a comprehensive perspective. We validate our system through quantitative experiments, a real-world case study, and user evaluations.
GLOF-Vulnerability Identification Based on Automatic Method for Mapping Glacial Lakes in Cordillera Blanca (Peru)
Glacial lake outburst floods (GLOFs) pose a significant threat to high-mountain communities and infrastructure, particularly in glacierized regions such as the Cordillera Blanca, Peru. This study presents an automated method for mapping glacial lakes using Sentinel-2 satellite imagery with enhanced band combinations and a segmentation-based foundational model (SAM 2.1). Our method enables consistent multitemporal lake extraction, which successfully mapped 80% of 448 manually identified glacial lakes. We performed a comparative analysis of images taken from May 2016 and May 2024, and identified five lakes potentially vulnerable to GLOFs. Notably, Lake Parón and Lake Piticocha experienced significant surface area expansion of 13.79 and 3.72 hectares, respectively. The other three lakes showed significally expansion despite its smaller size. GLOF simulations incorporating local topography and lake size indicate potential impacts on urban areas (e.g., Caraz city) and agricultural land. These findings highlight the importance of standarized and automated glacial lake monitoring for early risk assessment and disaster preparedness in the face of climate-driven glacial change.
Creating an Annotated Image Dataset for Wood Vessel Segmentation
Quantitative Wood Anatomy (QWA) is critical for understanding plant hydraulic function, ecological strategies, and environmental responses through the analysis of xylem anatomical traits. However, the advancement of automated image analysis in QWA has been hindered by the lack of large-scale annotated datasets, limiting the effective use of deep learning (DL) techniques. Existing datasets are small, domain-specific, and lack the diversity required for robust model generalization. This work addresses these limitations by introducing a large-scale, diverse annotated dataset derived from the Inside Wood repository. To establish an optimal annotation pipeline, we evaluated multiple semantic segmentation methods, which demonstrated that they trained on our dataset achieved a mean Intersection over Union (mIoU) exceeding 90%, significantly outperforming models from scratch, pre-trained on ImageNet and fine-tuned on target-domain data for a real-world scenario. This results validate our dataset and annotation methodology as a strong foundation for developing accurate and generalizable segmentation models for practical and industrial QWA applications.
MineTracker: Visual Analytics for Spatiotemporal Analysis of Mining Areas in the Brazilian Amazon
Conserving tropical forests is a global imperative, yet accelerating deforestation — driven in part by industrial mining — demands timely, fine-grained monitoring. Most existing platforms provide only coarse regional summaries, obscuring the emergence and growth of individual mines. We introduce MineTracker, a novel visual analytics tool that delivers site-level spatiotemporal insights into mining across the Brazilian Amazon. The system overlays annual mining footprints on key territorial boundaries (Indigenous lands, municipal limits) and applies time-series clustering to surface characteristic growth trajectories. Applied to Roraima, MineTracker revealed a pronounced surge of mining within legally protected Yanomami territory, illustrating its capacity to expose actionable trends and support evidence-based forest conservation and land governance decisions.
AquaFeat: A Features-Based Image Enhancement Model for Underwater Object Detection
The severe image degradation in underwater environments impairs object detection models, as traditional image enhancement methods are often not optimized for such downstream tasks. To address this, we propose AquaFeat, a novel, plug-and-play module that performs task-driven feature enhancement. Our approach integrates a multi-scale feature enhancement network trained end-to-end with the detector's loss function, ensuring the enhancement process is explicitly guided to refine features most relevant to the detection task. When integrated with YOLOv8m on challenging underwater datasets, AquaFeat achieves state-of-the-art Precision (0.877) and Recall (0.624), along with competitive m A P scores (mAP0.5 of 0.677 and mAP0.5:0.95 of 0.421). By delivering these accuracy gains while maintaining a practical processing speed of 46.5 FPS, our model provides an effective and computationally efficient solution for real-world applications, such as marine ecosystem monitoring and infrastructure inspection.
U-Net + Transformer Encoder and Lora Adaptation for Rural Sheep Segmentation
This study presents a lightweight and efficient sheep segmentation method by combining U-Net with Transformerbased encoders and Low-Rank Adaptation (LoRA). RGB videos of 99 sheep were collected using a custom framework with Kinect V2, generating a set of images. Three variants of U-Net were evaluated under identical training conditions. The MiTB5+LoRA model achieved an average IoU of 95.0%±0.04 with a reduced number of trainable parameters. The results show that the proposed approach balances accuracy and efficiency, making it suitable for deployment in resource-limited rural environments.
Open-Set Spatiotemporal Segmentation for Phenological Novelty Detection
Vegetation segmentation in natural ecosystems is challenged by class incompleteness, phenological ambiguity, and spatiotemporal variability. Annotated datasets often represent only a subset of temporal variations, leaving many phenological stages of known species unlabeled. Traditional segmentation models trained under closed-set assumptions struggle to generalize under such conditions. We present GeMOSS (Generative Models for Open-Set Spatiotemporal Segmentation), a novel extension of the GeMOS framework to pixel-level segmentation under open-set conditions. GeMOSS integrates a multibranch temporal convolutional network with shallow generative modeling to detect previously unseen temporal patterns within known vegetation classes. Rather than identifying taxonomically novel species, our approach targets phenological novelty within established classes, essential for ecological monitoring using partially labeled time series. Experiments on a real-world dataset from the Brazilian Cerrado demonstrate that GeMOSS achieves 96. 45% known class accuracy (compared to 94.36% for closed-set baselines), while filtering low-likelihood predictions potentially associated with atypical temporal behaviors, supporting its potential to flag underrepresented phenological instances in dynamic ecosystems.
Exploring Urban Factors with Autoencoders: Relationship Between Static and Dynamic Features
Urban analytics utilizes extensive datasets with diverse urban information to simulate, predict trends, and uncover complex patterns within cities. While these data enables advanced analysis, it also presents challenges due to its granularity, heterogeneity, and multimodality. To address these challenges, visual analytics tools have been developed to support the exploration of latent representations of fused heterogeneous and multimodal data, discretized at a street-level of detail. However, visualization-assisted tools seldom explore the extent to which fused data can offer deeper insights than examining each data source independently within an integrated visualization framework. In this work, we developed a visualization-assisted framework to analyze whether fused latent data representations are more effective than separate representations in uncovering patterns from dynamic and static urban data. The analysis reveals that combined latent representations produce more structured patterns, while separate ones are useful in particular cases.
MeshSeg: 3D Mesh Segmentation Through Color Clustering for Controllable Texture Editing
Segmenting 3D meshes is crucial for editing, reuse, and animation of 3D models, but it remains a labor-intensive manual process. We introduce MeshSeg, a 3D segmentation technique that operates on the 2D texture atlas of a 3D model. MeshSeg supports two segmentation strategies: a fast and lightweight classical image-processing pipeline, and a deep learning-based segmentation model (SAM). We encode each texture atlas segment as a color-based vector and merge visually similar regions using unsupervised clustering. An interactive interface further allows users to refine the segmentation through manual adjustments, ensuring high-quality, controllable results. MeshSeg's segmented outputs enable fast, targeted part-based texture editing (e.g., localized recoloring) in downstream applications such as animation, game development, and 3D asset production. By avoiding reliance on training data and focusing solely on color cues, our approach provides a general, easily reproducible solution for textured mesh segmentation across diverse models. We demonstrate its effectiveness on a series of geometrically complex models and show that it frees users from the time-consuming task of manually editing texture atlases.
E-MLNet: Enhanced Mutual Learning for Universal Domain Adaptation with Sample-Specific Weighting
Universal Domain Adaptation (UniDA) seeks to transfer knowledge from a labeled source to an unlabeled target domain without assuming any relationship between their label sets, requiring models to classify known samples while rejecting unknown ones. Advanced methods like Mutual Learning Network (MLNet) use a bank of one-vs-all classifiers adapted via Open-set Entropy Minimization (OEM). However, this strategy treats all classifiers equally, diluting the learning signal. We propose the Enhanced Mutual Learning Network (E-MLNet), which integrates a dynamic weighting strategy to OEM. By leveraging the closed-set classifier's predictions, E-MLNet focuses adaptation on the most relevant class boundaries for each target sample, sharpening the distinction between known and unknown classes. We conduct extensive experiments on four challenging benchmarks: Office-31, Office-Home, VisDA-2017, and ImageCLEF. The results demonstrate that E-MLNet achieves the highest average H-scores on VisDA and ImageCLEF and exhibits superior robustness over its predecessor. E-MLNet outperforms the strong MLNet baseline in the majority of individual adaptation tasks—22 out of 31 in the challenging Open-Partial DA setting and 19 out of 31 in the Open-Set DA setting—confirming the benefits of our focused adaptation strategy.