Context-Semantic Quality Awareness Network for Fine-Grained Visual
  Categorization
Qin Xu Sitong Li Jiahui Wang Bo Jiang Jinhui Tang
Exploring and mining subtle yet distinctive features between sub-categories
with similar appearances is crucial for fine-grained visual categorization
(FGVC). However, less effort has been devoted to assessing the quality of
extracted visual representations. Intuitively, the network may struggle to
capture discriminative features from low-quality samples, which leads to a
significant decline in FGVC performance. To tackle this challenge, we propose a
weakly supervised Context-Semantic Quality Awareness Network (CSQA-Net) for
FGVC. In this network, to model the spatial contextual relationship between
rich part descriptors and global semantics for capturing more discriminative
details within the object, we design a novel multi-part and multi-scale
cross-attention (MPMSCA) module. Before feeding to the MPMSCA module, the part
navigator is developed to address the scale confusion problems and accurately
identify the local distinctive regions. Furthermore, we propose a generic
multi-level semantic quality evaluation module (MLSQE) to progressively
supervise and enhance hierarchical semantics from different levels of the
backbone network. Finally, context-aware features from MPMSCA and semantically
enhanced features from MLSQE are fed into the corresponding quality probing
classifiers to evaluate their quality in real-time, thus boosting the
discriminability of feature representations. Comprehensive experiments on four
popular and highly competitive FGVC datasets demonstrate the superiority of the
proposed CSQA-Net in comparison with the state-of-the-art methods.