{
  "id": "1912.01230",
  "version": "v1",
  "published": "2019-12-03T07:46:47.000Z",
  "updated": "2019-12-03T07:46:47.000Z",
  "title": "Hi-CMD: Hierarchical Cross-Modality Disentanglement for Visible-Infrared Person Re-Identification",
  "authors": [
    "Seokeon Choi",
    "Sumin Lee",
    "Youngeun Kim",
    "Taekyung Kim",
    "Changick Kim"
  ],
  "categories": [
    "cs.CV"
  ],
  "abstract": "Visible-infrared person re-identification (VI-ReID) is an important task in night-time surveillance applications, since visible cameras are difficult to capture valid appearance information under poor illumination conditions. Compared to traditional person re-identification that handles only the intra-modality discrepancy, VI-ReID suffers from additional cross-modality discrepancy caused by different types of imaging systems. To reduce both intra- and cross-modality discrepancies, we propose a Hierarchical Cross-Modality Disentanglement (Hi-CMD) method, which automatically disentangles ID-discriminative factors and ID-excluded factors from visible-thermal images. We only use ID-discriminative factors for robust cross-modality matching without ID-excluded factors such as pose or illumination. To implement our approach, we introduce an ID-preserving person image generation network and a hierarchical feature learning module. Our generation network learns the disentangled representation by generating a new cross-modality image with different poses and illuminations while preserving a person's identity. At the same time, the feature learning module enables our model to explicitly extract the common ID-discriminative characteristic between visible-infrared images. Extensive experimental results demonstrate that our method outperforms the state-of-the-art methods on two VI-ReID datasets.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-12-03T07:46:47.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "visible-infrared person re-identification",
      "hierarchical cross-modality disentanglement",
      "feature learning module",
      "capture valid appearance information",
      "id-preserving person image generation network"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}