Summary of Stitch Contrast and Segment_learning a Human Action Segmentation Model Using Trimmed Skeleton Videos, by Haitao Tian et al.
Stitch Contrast and Segment_Learning a Human Action Segmentation Model Using Trimmed Skeleton Videos
by Haitao Tian, Pierre Payeur
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This novel framework introduces a three-step approach, comprising Stitch, Contrast, and Segment, for skeleton-based action segmentation trained on short trimmed videos that can run on longer un-trimmed videos. The Stitch step generates multi-action stitched sequences by treating trimmed skeleton videos as elementary human motions. Contrast learns contrastive representations from these sequences using a novel discrimination pretext task, enabling the skeleton encoder to learn meaningful action-temporal contexts. The Segment step relates the proposed method to action segmentation, learning a segmentation layer while handling particular data availability. This framework is evaluated on a trimmed source dataset and an un-trimmed target dataset in an adaptation formulation for real-world skeleton-based human action segmentation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research develops a new way to recognize human actions from video recordings of skeletons. Right now, most methods rely on short videos with clear actions, but this limits their use in real-life situations where we have longer, more complicated videos. The solution involves three steps: first, the “Stitch” part connects shorter videos together into longer ones that include multiple actions. Next, the “Contrast” step teaches a computer to recognize patterns and learn from these stitched sequences. Finally, the “Segment” step uses this learning to separate individual actions within a longer video. This method is tested on both short and long videos and shows promising results for recognizing human actions in real-world scenarios. |
Keywords
» Artificial intelligence » Encoder
