Papers › Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network...
Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-grained Image Recognition
Heliang Zheng, Jianlong Fu, Zheng-Jun Zha, Jiebo Luo
Learning subtle yet discriminative features (e.g., beak and eyes for a bird) plays a significant role in fine-grained image recognition. Existing attention-based approaches localize and amplify significant parts to learn fine-grained details, which often suffer from a limited number of parts and heavy computational cost. In this paper, we propose to learn such fine-grained features from hundreds of part proposals by Trilinear Attention Sampling Network (TASN) in an efficient teacher-student manner. Specifically, TASN consists of 1) a trilinear attention module, which generates attention maps by modeling the inter-channel relationships, 2) an attention-based sampler which highlights attended parts with high resolution, and 3) a feature distiller, which distills part features into a global one by weight sharing and feature preserving strategies. Extensive experiments verify that TASN yields the best performance under the same settings with the most competitive approaches, in iNaturalist-2017, CUB-Bird, and Stanford-Cars datasets.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Fine-Grained Image Classification | CUB-200-2011 | TASN | Accuracy | 87.9 | #23 of 30 | Archive leaderboard | report |
| Fine-Grained Image Classification | Stanford Cars | TASN | Accuracy | 93.8% | #61 of 83 | Archive leaderboard | report |
| Fine-Grained Image Classification | iNaturalist | TASN | Top 1 Accuracy | 68.2 | #1 of 1 | Archive leaderboard | report |
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