Summary of One Token to Seg Them All: Language Instructed Reasoning Segmentation in Videos, by Zechen Bai et al.
One Token to Seg Them All: Language Instructed Reasoning Segmentation in Videos
by Zechen Bai, Tong He, Haiyang Mei, Pichao Wang, Ziteng Gao, Joya Chen, Lei Liu, Zheng Zhang, Mike Zheng Shou
First submitted to arxiv on: 29 Sep 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 A large language model is introduced that tackles the problem of segmenting objects in videos based on language instructions. The model, called VideoLISA, uses a combination of natural language processing and computer vision techniques to generate temporally consistent segmentation masks in videos. Unlike existing image-based methods, VideoLISA can handle the additional temporal dimension of videos by balancing temporal context and spatial detail within computational constraints. The model is evaluated on diverse benchmarks, including a newly introduced ReasonVOS benchmark, and demonstrates superior performance in video object segmentation tasks involving complex reasoning, temporal understanding, and object tracking. The paper also shows promising generalization to image segmentation, revealing the potential of VideoLISA as a unified foundation model for language-instructed object segmentation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VideoLISA is a new way to help computers understand videos better. It uses a combination of language and computer vision techniques to identify objects in videos and track them over time. This is useful because existing methods that work well with images struggle with videos, which have an extra dimension (time). VideoLISA solves this problem by balancing the importance of past and future information when making decisions. The model is tested on many different types of videos and performs well, even when the objects being tracked are complex or moving quickly. |
Keywords
» Artificial intelligence » Generalization » Image segmentation » Large language model » Natural language processing » Object tracking
