Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-grained Image Recognition

CVPR 2019 Heliang ZhengJianlong FuZheng-Jun ZhaJiebo 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... (read more)

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Fine-Grained Image Classification CUB-200-2011 TASN Accuracy 87.9% # 17

Methods used in the Paper


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