Saliency-Guided Model with Cross and Channel Attention for Few-Shot Semantic Segmentation
Resumo
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.
