Title | ||
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CA-PMG: Channel attention and progressive multi-granularity training network for fine-grained visual classification |
Abstract | ||
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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. |
Year | DOI | Venue |
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2021 | 10.1049/ipr2.12238 | IET IMAGE PROCESSING |
DocType | Volume | Issue |
Journal | 15 | 14 |
ISSN | Citations | PageRank |
1751-9659 | 0 | 0.34 |
References | Authors | |
0 | 6 |
Name | Order | Citations | PageRank |
---|---|---|---|
Peipei Zhao | 1 | 1 | 1.70 |
Qiguang Miao | 2 | 355 | 49.69 |
Hang Yao | 3 | 0 | 0.68 |
Xiangzeng Liu | 4 | 0 | 1.69 |
Ruyi Liu | 5 | 2 | 1.41 |
Maoguo Gong | 6 | 2676 | 172.02 |