{
  "id": "2211.09788",
  "version": "v1",
  "published": "2022-11-17T18:56:19.000Z",
  "updated": "2022-11-17T18:56:19.000Z",
  "title": "DiffusionDet: Diffusion Model for Object Detection",
  "authors": [
    "Shoufa Chen",
    "Peize Sun",
    "Yibing Song",
    "Ping Luo"
  ],
  "comment": "Tech report. Code is available at https://github.com/ShoufaChen/DiffusionDet",
  "categories": [
    "cs.CV"
  ],
  "abstract": "We propose DiffusionDet, a new framework that formulates object detection as a denoising diffusion process from noisy boxes to object boxes. During training stage, object boxes diffuse from ground-truth boxes to random distribution, and the model learns to reverse this noising process. In inference, the model refines a set of randomly generated boxes to the output results in a progressive way. The extensive evaluations on the standard benchmarks, including MS-COCO and LVIS, show that DiffusionDet achieves favorable performance compared to previous well-established detectors. Our work brings two important findings in object detection. First, random boxes, although drastically different from pre-defined anchors or learned queries, are also effective object candidates. Second, object detection, one of the representative perception tasks, can be solved by a generative way. Our code is available at https://github.com/ShoufaChen/DiffusionDet.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2022-11-17T18:56:19.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "diffusion model",
      "formulates object detection",
      "object boxes diffuse",
      "diffusiondet achieves favorable performance",
      "effective object candidates"
    ],
    "tags": [
      "github project"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}