{
  "id": "1409.6155",
  "version": "v1",
  "published": "2014-09-22T12:42:08.000Z",
  "updated": "2014-09-22T12:42:08.000Z",
  "title": "1-HKUST: Object Detection in ILSVRC 2014",
  "authors": [
    "Cewu Lu",
    "Hao Chen",
    "Qifeng Chen",
    "Hei Law",
    "Yao Xiao",
    "Chi-Keung Tang"
  ],
  "comment": "3 pages",
  "categories": [
    "cs.CV"
  ],
  "abstract": "The Imagenet Large Scale Visual Recognition Challenge (ILSVRC) is the one of the most important big data challenges to date. We participated in the object detection track of ILSVRC 2014 and received the fourth place among the 38 teams. We introduce in our object detection system a number of novel techniques in localization and recognition. For localization, initial candidate proposals are generated using selective search, and a novel bounding boxes regression method is used for better object localization. For recognition, to represent a candidate proposal, we adopt three features, namely, RCNN feature, IFV feature, and DPM feature. Given these features, category-specific combination functions are learned to improve the object recognition rate. In addition, object context in the form of background priors and object interaction priors are learned and applied in our system. Our ILSVRC 2014 results are reported alongside with the results of other participating teams.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2014-09-22T12:42:08.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "object detection",
      "imagenet large scale visual recognition",
      "large scale visual recognition challenge",
      "novel bounding boxes regression method",
      "important big data challenges"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 3,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2014arXiv1409.6155L"
    }
  }
}