Diffusion 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.
PDF Abstract ICCV 2023 PDF ICCV 2023 Abstract
Breakfast
GTEA
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Action Segmentation | 50 Salads | DiffAct | F1@10% | 90.1 | # 3 | |
| Edit | 85.0 | # 3 | ||||
| Acc | 88.9 | # 3 | ||||
| F1@25% | 89.2 | # 3 | ||||
| F1@50% | 83.7 | # 3 | ||||
| Action Segmentation | Breakfast | DiffAct | F1@10% | 80.3 | # 3 | |
| F1@50% | 64.6 | # 3 | ||||
| Acc | 76.4 | # 3 | ||||
| Edit | 78.4 | # 3 | ||||
| F1@25% | 75.9 | # 2 | ||||
| Action Segmentation | GTEA | DiffAct | F1@10% | 92.5 | # 6 | |
| F1@50% | 84.7 | # 2 | ||||
| Acc | 82.2 | # 3 | ||||
| Edit | 89.6 | # 6 | ||||
| F1@25% | 91.5 | # 5 |
