Summary of Towards Neuro-symbolic Video Understanding, by Minkyu Choi et al.
Towards Neuro-Symbolic Video Understanding
by Minkyu Choi, Harsh Goel, Mohammad Omama, Yunhao Yang, Sahil Shah, Sandeep Chinchali
First submitted to arxiv on: 16 Mar 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 paper proposes a system for efficient video frame retrieval, addressing the lack of long-term temporal reasoning in current foundation models like VideoLLaMA and ViCLIP. The authors argue that decoupling per-frame perception from temporal reasoning is crucial for effective scene identification. They leverage vision-language models for semantic understanding and employ state machines and temporal logic formulae to reason about long-term event evolution, achieving a 9-15% F1 score improvement on complex event identification tasks in datasets like Waymo and NuScenes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve the problem of extracting meaningful frames from videos. It’s trying to make video processing better by separating two important parts: understanding what’s happening in each frame, and thinking about how those events are connected over time. The idea is that if we can do these things separately, we’ll get more accurate results. |
Keywords
» Artificial intelligence » F1 score
