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.

翻译：探索并挖掘具有相似外观的子类别之间细微而独特的特征对于细粒度视觉分类（FGVC）至关重要。然而，目前对提取的视觉表示质量的评估工作较少。直观上，网络可能难以从低质量样本中捕获判别性特征，从而导致FGVC性能显著下降。为解决这一挑战，我们提出了一种弱监督的上下文语义质量感知网络（CSQA-Net）用于FGVC。在该网络中，为建模丰富部件描述符与全局语义之间的空间上下文关系，以捕获对象内部更具判别性的细节，我们设计了一个新颖的多部件多尺度交叉注意力（MPMSCA）模块。在将特征输入MPMSCA模块之前，开发了部件导航器以解决尺度混淆问题并准确定位局部显著区域。此外，我们提出了一种通用的多级语义质量评估模块（MLSQE），以逐步监督并增强来自骨干网络不同层级的层次化语义。最后，将来自MPMSCA的上下文感知特征与来自MLSQE的语义增强特征输入对应的质量探测分类器，实时评估其质量，从而提升特征表示的判别能力。在四个广泛使用且极具竞争力的FGVC数据集上的综合实验表明，所提出的CSQA-Net相比现有最先进方法具有优越性。