Improving Fine-Grained Vehicle Classification via Multitask Learning and Hierarchical Consistency - Lima, Gabriel E.; Santos, Eduardo; Nascimento Jr., Eduil; Laroca, Rayson; Menotti, David (Anais Estendidos da Conference on Graphics, Patterns and Images (SIBGRAPI); 2025: Anais Estendidos da XXXVIII Conference on Graphics, Patterns and Images, 2025)
Fine-Grained Vehicle Classification (FGVC) plays a key role in intelligent transportation systems, enabling the recognition of vehicle attributes – such as type, make, and model – from images. Such information supports vehicle identification and can complement automatic license plate recognition by enabling cross-checks and addressing cases with unreadable plates. However, existing approaches often treat these attributes independently, overlooking their hierarchical relationships and differences in task difficulty. This work-in-progress study explores the use of Multitask Learning (MTL) and hierarchical regularization to address these gaps. We evaluate seven deep learning models on a diverse dataset under three training setups: singletask learning, MTL with balanced optimization, and MTL with hierarchical regularization. Results show that MTL consistently improves classification accuracy, while incorporating hierarchical information significantly reduces semantic inconsistencies and enhances confidence calibration. In our best-performing configuration, hierarchy-violating errors dropped from 32.87% (singletask) to 4.10% (MTL with hierarchical regularization). These findings highlight the importance of modeling semantic relationships among attributes in FGVC and suggest promising directions for building more accurate and reliable classifiers. Future work will expand attribute granularity, investigate optimal task combinations, and benchmark against state-of-the-art methods.