Understanding long-form videos requires precise temporal action segmentation. While existing studies typically employ multi-stage models that follow an iterative refinement process, we present a novel framework based on the denoising diffusion model that retains this core iterative principle. Within this framework, the model iteratively produces action predictions starting with random noise, conditioned on the features of the input video. To effectively capture three key characteristics of human actions, namely the position prior, the boundary ambiguity, and the relational dependency, we propose a cohesive masking strategy for the conditioning features. Moreover, a consistency gradient guidance technique is proposed, which maximizes the similarity between outputs with or without the masking, thereby enriching conditional information during the inference process. Extensive experiments are performed on four datasets, i.e., GTEA, 50Salads, Breakfast, and Assembly101. The results indicate that our proposed method outperforms or is on par with existing state-of-the-art techniques, underscoring the potential of generative approaches for action segmentation.

Temporal action segmentation is crucial for understanding long-form videos. Previous works on this task commonly adopt an iterative refinement paradigm by using multi-stage models. We propose a novel framework via denoising diffusion models, which nonetheless shares the same inherent spirit of such iterative refinement. In this framework, action predictions are iteratively generated from random noise with input video features as conditions. To enhance the modeling of three striking characteristics of human actions, including the position prior, the boundary ambiguity, and the relational dependency, we devise a unified masking strategy for the conditioning inputs in our framework. Extensive experiments on three benchmark datasets, i.e., GTEA, 50Salads, and Breakfast, are performed and the proposed method achieves superior or comparable results to state-of-the-art methods, showing the effectiveness of a generative approach for action segmentation. Code is at tinyurl.com/DiffAct.

Surgical skills have a great influence on surgical safety and patients’ well-being. Traditional assessment of surgical skills involves strenuous manual efforts, which lacks efficiency and repeatability. Therefore, we attempt to automatically predict how well the surgery is performed using the surgical video. In this paper, a unified multi-path framework for automatic surgical skill assessment is proposed, which takes care of multiple composing aspects of surgical skills, including surgical tool usage, intraoperative event pattern, and other skill proxies. The dependency relationships among these different aspects are specially modeled by a path dependency module in the framework. We conduct extensive experiments on the JIGSAWS dataset of simulated surgical tasks, and a new clinical dataset of real laparoscopic surgeries. The proposed framework achieves promising results on both datasets, with the state-of-the-art on the simulated dataset advanced from 0.71 Spearman’s correlation to 0.80. It is also shown that combining multiple skill aspects yields better performance than relying on a single aspect.