  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
https://github.com/sauradip/DiffusionTAD.
