“DiffusionDet: Diffusion Model for Object Detection”, Shoufa Chen, Peize Sun, Yibing Song, Ping Luo2022-11-17 (diffusion model)⁠:

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 Github.