DiffusionDet: Diffusion Model for Object Detection

Shoufa Chen, Peize Sun, Yibing Song, Ping Luo; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 19830-19843


We propose DiffusionDet, a new framework that formulates object detection as a denoising diffusion process from noisy boxes to object boxes. During the 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. Our work possesses an appealing property of flexibility, which enables the dynamic number of boxes and iterative evaluation. The extensive experiments on the standard benchmarks show that DiffusionDet achieves favorable performance compared to previous well-established detectors. For example, DiffusionDet achieves 5.3 AP and 4.8 AP gains when evaluated with more boxes and iteration steps, under a zero-shot transfer setting from COCO to CrowdHuman. Our code is available at https://github.com/ShoufaChen/DiffusionDet.

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@InProceedings{Chen_2023_ICCV, author = {Chen, Shoufa and Sun, Peize and Song, Yibing and Luo, Ping}, title = {DiffusionDet: Diffusion Model for Object Detection}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2023}, pages = {19830-19843} }