We propose a new formulation of temporal action detection (TAD) with
denoising diffusion, DiffTAD in short. Taking as input random temporal
proposals, it can yield action proposals accurately given an untrimmed long
video. This presents a generative modeling perspective, against previous
discriminative learning manners. This capability is achieved by first diffusing
the ground-truth proposals to random ones (i.e., the forward/noising process)
and then learning to reverse the noising process (i.e., the backward/denoising
process). Concretely, we establish the denoising process in the Transformer
decoder (e.g., DETR) by introducing a temporal location query design with
faster convergence in training. We further propose a cross-step selective
conditioning algorithm for inference acceleration. Extensive evaluations on
ActivityNet and THUMOS show that our DiffTAD achieves top performance compared
to previous art alternatives. The code will be made available at
this https URL.

本文提出了一种使用扩散去噪的新时域动作检测（TAD）方法，名为 DiffTAD。该方法采用生成建模方法，而非以往判别式学习方法，借助 Transformer decoder 中引入的时间位置查询设计来建立去噪过程，通过交叉步选择性调节算法来提高推断速度。在 ActivityNet 和 THUMOS 上广泛评估后，我们的 DiffTAD 相比以往的艺术选择方案，表现出卓越的性能。