Context-Semantic Quality Awareness Network for fine-grained visual categorization

Exploring and mining subtle yet distinctive features between sub-categories with similar appearances is crucial for fine-grained visual categorization (FGVC). However, the existing FGVC methods cannot mine discriminative features from low-quality samples, leading to a significant decline in performance. To address this issue, we propose a weakly supervised Context-Semantic Quality Awareness Network (CSQA-Net) for FGVC. Specifically, to assess and enhance the quality of multi-granularity visual representations, we propose the Multi-level Semantic Quality Evaluation (MSQE) module, composed of the Quality Probing (QP) classifier. To alleviate the scale confusion problems and accurately identify the local distinctive regions, the part navigator is developed. Moreover, the Multi-part and Multi-scale Cross-Attention (MMCA) module is designed to model the spatial contextual relationship between rich part descriptors and global semantics, thus capturing more discriminative details within the object. Finally, the context-aware features from MMCA and semantically enhanced features from MSQE are fed into the corresponding QP classifiers to evaluate the quality in real time, further boosting the discriminability. Comprehensive experiments on four popular and highly competitive datasets demonstrate the superiority of the proposed CSQA-Net in comparison with the state-of-the-art methods. Code is available at https://github.com/zmisiter/CSQA-Net.