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

Heliang Zheng, Jianlong Fu, Zheng-Jun Zha, Jiebo Luo
2019 arXiv   pre-print
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
more » ... er. 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.
arXiv:1903.06150v2 fatcat:5nfti7jptzhnllvq3uudh7x5aa