Summary of Videoorion: Tokenizing Object Dynamics in Videos, by Yicheng Feng et al.
VideoOrion: Tokenizing Object Dynamics in Videos
by Yicheng Feng, Yijiang Li, Wanpeng Zhang, Hao Luo, Zihao Yue, Sipeng Zheng, Zongqing Lu
First submitted to arxiv on: 25 Nov 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 The paper presents a new Video Large Language Model (Video-LLM) called VideoOrion, which focuses on capturing the key semantic information in videos. This is achieved by using expert vision models to extract object dynamics through a detect-segment-track pipeline, and encoding them into a set of object tokens that can be understood by LLMs. The method addresses the challenge of efficiently compressing high-dimensional video data into semantic tokens, allowing for better representation and performance on tasks such as video-based referring. VideoOrion is able to accomplish this by using a more natural and efficient approach than previous methods, which often result in information loss or entangled semantics. The object tokens also enable VideoOrion to perform well on tasks that require explicit object modeling of video content. Experimental results show that VideoOrion achieves competitive results on both general video question answering and video-based referring benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VideoOrion is a new way for computers to understand videos by breaking them down into smaller pieces called “object tokens”. This helps the computer learn about the things happening in the video, like where objects are moving or staying still. The new model is better than old methods because it doesn’t lose important information and can understand the video’s meaning more clearly. The model does this by using other computer vision models to identify objects and track their movements. This helps VideoOrion learn about the objects in the video, like animals or cars, and how they move over time. The object tokens are then used to help the computer answer questions about what is happening in the video. The results show that VideoOrion can do this well on different types of videos and tasks. |
Keywords
» Artificial intelligence » Large language model » Question answering » Semantics
