{
  "id": "1907.01430",
  "version": "v1",
  "published": "2019-07-02T15:12:20.000Z",
  "updated": "2019-07-02T15:12:20.000Z",
  "title": "Where are the Masks: Instance Segmentation with Image-level Supervision",
  "authors": [
    "Issam H. Laradji",
    "David Vazquez",
    "Mark Schmidt"
  ],
  "comment": "Accepted at BMVC2019",
  "categories": [
    "cs.CV",
    "cs.LG",
    "eess.IV"
  ],
  "abstract": "A major obstacle in instance segmentation is that existing methods often need many per-pixel labels in order to be effective. These labels require large human effort and for certain applications, such labels are not readily available. To address this limitation, we propose a novel framework that can effectively train with image-level labels, which are significantly cheaper to acquire. For instance, one can do an internet search for the term \"car\" and obtain many images where a car is present with minimal effort. Our framework consists of two stages: (1) train a classifier to generate pseudo masks for the objects of interest; (2) train a fully supervised Mask R-CNN on these pseudo masks. Our two main contribution are proposing a pipeline that is simple to implement and is amenable to different segmentation methods; and achieves new state-of-the-art results for this problem setup. Our results are based on evaluating our method on PASCAL VOC 2012, a standard dataset for weakly supervised methods, where we demonstrate major performance gains compared to existing methods with respect to mean average precision.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-07-02T15:12:20.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "instance segmentation",
      "image-level supervision",
      "demonstrate major performance gains",
      "generate pseudo masks",
      "large human effort"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}