{
  "id": "2301.12436",
  "version": "v1",
  "published": "2023-01-29T12:29:24.000Z",
  "updated": "2023-01-29T12:29:24.000Z",
  "title": "Team VI-I2R Technical Report on EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2022",
  "authors": [
    "Yi Cheng",
    "Dongyun Lin",
    "Fen Fang",
    "Hao Xuan Woon",
    "Qianli Xu",
    "Ying Sun"
  ],
  "categories": [
    "cs.CV"
  ],
  "abstract": "In this report, we present the technical details of our submission to the EPIC-KITCHENS-100 Unsupervised Domain Adaptation (UDA) Challenge for Action Recognition 2022. This task aims to adapt an action recognition model trained on a labeled source domain to an unlabeled target domain. To achieve this goal, we propose an action-aware domain adaptation framework that leverages the prior knowledge induced from the action recognition task during the adaptation. Specifically, we disentangle the source features into action-relevant features and action-irrelevant features using the learned action classifier and then align the target features with the action-relevant features. To further improve the action prediction performance, we exploit the verb-noun co-occurrence matrix to constrain and refine the action predictions. Our final submission achieved the first place in terms of top-1 action recognition accuracy.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2023-01-29T12:29:24.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "action recognition",
      "team vi-i2r technical report",
      "unsupervised domain adaptation challenge",
      "action prediction",
      "action-relevant features"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}