  Fine-grained visual classification (FGVC) is becoming an important research
field, due to its wide applications and the rapid development of computer
vision technologies. The current state-of-the-art (SOTA) methods in the FGVC
usually employ attention mechanisms to first capture the semantic parts and
then discover their subtle differences between distinct classes. The
channel-spatial attention mechanisms, which focus on the discriminative
channels and regions simultaneously, have significantly improved the
classification performance. However, the existing attention modules are poorly
guided since part-based detectors in the FGVC depend on the network learning
ability without the supervision of part annotations. As obtaining such part
annotations is labor-intensive, some visual localization and explanation
methods, such as gradient-weighted class activation mapping (Grad-CAM), can be
utilized for supervising the attention mechanism. We propose a Grad-CAM guided
channel-spatial attention module for the FGVC, which employs the Grad-CAM to
supervise and constrain the attention weights by generating the coarse
localization maps. To demonstrate the effectiveness of the proposed method, we
conduct comprehensive experiments on three popular FGVC datasets, including
CUB-$200$-$2011$, Stanford Cars, and FGVC-Aircraft datasets. The proposed
method outperforms the SOTA attention modules in the FGVC task. In addition,
visualizations of feature maps also demonstrate the superiority of the proposed
method against the SOTA approaches.
