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Complemental Attention Multi-Feature Fusion Network for Fine-Grained Classification
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DOI：10.1109/LSP.2021.3114622.png)
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摘要
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Transformer-based architecture network has shown excellent performance in the coarse-grained image classification. However, it remains a challenge for the fine-grained image classification task, which needs more significant regional information. As one of the attention mechanisms, transformer pays attention to the most significant region while neglecting other sub-significant regions. To use more regional information, in this letter, we propose a complemental attention multi-feature fusion network (CAMF), which extracts multiple attention features to obtain more effective features. In CAMF, we propose two novel modules: (i) a complemental attention module (CAM) that extracts the most salient attention feature and the complemental attention feature. (ii) a multi-feature fusion module (MFM) that uses different branches to extract multiple regional discriminative features. Furthermore, a new feature similarity loss is proposed to measure the diversity of inter-class features. Experiments were conducted on four public fine-grained classification datasets. Our CAMF achieves 91.2%, 92.8%, 93.3%, 95.3% on CUB-200-2011, Stanford Dogs, FGVC-Aircraft, and Stanford Cars. The ablation study verified that CAM and MFM can focus on more local discriminative regions and improve fine-grained classification performance.
Keyword:
Feature extraction
Transformers
Automobiles
Task analysis
Dogs
Training
Loss measurement
Attention
fine-grained classification
transformer
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
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