Summary of General and Task-oriented Video Segmentation, by Mu Chen et al.
General and Task-Oriented Video Segmentation
by Mu Chen, Liulei Li, Wenguan Wang, Ruijie Quan, Yi Yang
First submitted to arxiv on: 9 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed GvSeg framework presents a general solution for addressing various video segmentation tasks, including instance, semantic, panoptic, and exemplar-guided ones. This framework maintains an identical architectural design while providing innovations that adapt to the specific requirements of each task. The approach includes holistic disentanglement and modeling of segment targets based on appearance, position, and shape, as well as reformulated query initialization, matching, and sampling strategies. Experimental results on seven benchmark datasets show that GvSeg outperforms existing specialized and general solutions by a significant margin. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GvSeg is a new way to break down videos into meaningful pieces. It can be used for different tasks like finding specific objects or understanding video scenes. Right now, there are many different ways to do this, but they’re all designed for one specific task. GvSeg changes this by being able to adapt to any of these tasks, making it a more powerful tool. |
