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.

为了解决细粒度视觉分类中的品质问题，本文提出了一种弱监督的上下文 - 语义质量感知网络（CSQA-Net），其中使用多部分和多尺度交叉注意力模块（MPMSCA）来捕捉目标内部更具区别性的细节，提出了一种层次化的多级语义质量评估模块（MLSQE）来逐渐监督和强化骨干网络中的分层语义，最终通过对特征表示的质量进行实时评估，提升了其可辨识性。在四个广泛使用的细粒度视觉分类数据集上进行的综合实验结果表明，提出的 CSQA-Net 优于现有的方法。