CA‐PMG: Channel attention and progressive multi‐granularity training network for fine‐grained visual classification

Abstract

Abstract Fine‐grained visual classification is challenging due to the inherently subtle intra‐class object variations. To solve this issue, a novel framework named channel attention and progressive multi‐granularity training network, is proposed. It first exploits meaningful feature maps through the channel attention module and captures multi‐granularity features by the progressive multi‐granularity training module. For each feature map, the channel attention module is proposed to explore channel‐wise correlation. This allows the model to re‐weight the channels of the feature map according to the impact of their semantic information on performance. Furthermore, the progressive multi‐granularity training module is introduced to fuse features cross multi‐granularity. And the fused features pay more attention to the subtle differences between images. The model can be trained efficiently in an end‐to‐end manner without bounding box or part annotations. Finally, comprehensive experiments are conducted to show that the method achieves state‐of‐the‐art performances on the CUB‐200‐2011, Stanford Cars, and FGVC‐Aircraft datasets. Ablation studies demonstrate the effectiveness of each part in our module.

Keywords

• Optical, image and video signal processing
• Image recognition
• Computer vision and image processing techniques