CA-PMG: Channel attention and progressive multi-granularity training network for fine-grained visual classification
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 themodel to re-weight the channels of the feature map according to the impact of their semantic information on performance. Furthermore, the progressivemulti-granularity trainingmodule 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.
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CUB-200-2011
Stanford Cars
FGVC-Aircraft
