论文标题
扩散码:对象检测的扩散模型
DiffusionDet: Diffusion Model for Object Detection
论文作者
论文摘要
我们提出了一个新框架diffusiondet,该框架将对象检测定为从嘈杂框到对象框的deno式扩散过程。在训练阶段,对象框从基地框到随机分布扩散,该模型学会了扭转这个尖锐的过程。在推断中,模型以渐进的方式将一组随机生成的框提炼成输出结果。我们的作品具有灵活性的吸引力,可以动态数量的框和迭代评估。标准基准测试的广泛实验表明,与以前已建立的探测器相比,扩散模具具有优惠的性能。例如,在用更多的盒子和迭代步骤进行评估时,在从可可到达人类的零射击转移设置下进行评估时,diffusionDet可实现5.3 AP和4.8 AP的增长。我们的代码可在https://github.com/shoufachen/diffusiondet上找到。
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.