{
  "id": "2110.14711",
  "version": "v3",
  "published": "2021-10-27T18:55:47.000Z",
  "updated": "2022-08-23T08:38:52.000Z",
  "title": "A Survey of Self-Supervised and Few-Shot Object Detection",
  "authors": [
    "Gabriel Huang",
    "Issam Laradji",
    "David Vazquez",
    "Simon Lacoste-Julien",
    "Pau Rodriguez"
  ],
  "comment": "To appear in IEEE Transactions on Pattern Analysis and Machine Intelligence. Awesome Few-Shot Object Detection (Leaderboard) at https://github.com/gabrielhuang/awesome-few-shot-object-detection",
  "categories": [
    "cs.CV",
    "cs.AI",
    "cs.LG"
  ],
  "abstract": "Labeling data is often expensive and time-consuming, especially for tasks such as object detection and instance segmentation, which require dense labeling of the image. While few-shot object detection is about training a model on novel (unseen) object classes with little data, it still requires prior training on many labeled examples of base (seen) classes. On the other hand, self-supervised methods aim at learning representations from unlabeled data which transfer well to downstream tasks such as object detection. Combining few-shot and self-supervised object detection is a promising research direction. In this survey, we review and characterize the most recent approaches on few-shot and self-supervised object detection. Then, we give our main takeaways and discuss future research directions. Project page at https://gabrielhuang.github.io/fsod-survey/",
  "revisions": [
    {
      "version": "v3",
      "updated": "2022-08-23T08:38:52.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "few-shot object detection",
      "self-supervised object detection",
      "project page",
      "self-supervised methods aim",
      "downstream tasks"
    ],
    "tags": [
      "github project"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}