{
  "id": "1903.06150",
  "version": "v1",
  "published": "2019-03-14T17:52:18.000Z",
  "updated": "2019-03-14T17:52:18.000Z",
  "title": "Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-grained Image Recognition",
  "authors": [
    "Heliang Zheng",
    "Jianlong Fu",
    "Zheng-Jun Zha",
    "Jiebo Luo"
  ],
  "categories": [
    "cs.CV"
  ],
  "abstract": "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-03-14T17:52:18.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "learning trilinear attention sampling network",
      "fine-grained image recognition",
      "generates attention maps",
      "heavy computational cost"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}