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DiffTAD: Temporal Action Detection with Proposal Denoising Diffusion

DiffTAD is the first formulation of diffusion model for Temporal Action Detection task.

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DiffTAD: Temporal Action Detection with Proposal Denoising Diffusion
Sauradip Nag, Xiatian Zhu, Jiankang Deng, Yi-Zhe Song, Tao Xiang
arXiv 2303.14863

Updates

Summary

Abstract

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.

Architecture

<p align="center"> <img src="assets/fig2_v4.png" width="800" height="600" > </p>

Qualitative Visualization

<p align="center"> <img src="assets/DiffTAD_visual_v2.png" > </p>

Citing DiffTAD

If you use DiffTAD in your research or wish to refer to the baseline results published here, please use the following BibTeX entry.

@article{nag2023difftad,
      title={DiffTAD: Temporal Action Detection with Proposal Denoising Diffusion},
      author={Sauradip Nag and Xiatian Zhu and Jiankang Deng and Yi-Zhe Song and Tao Xiang},
      journal={arXiv preprint arXiv:2303.14863},
      year={2023}
}