Summary of Enhancing Human-centered Dynamic Scene Understanding Via Multiple Llms Collaborated Reasoning, by Hang Zhang et al.
Enhancing Human-Centered Dynamic Scene Understanding via Multiple LLMs Collaborated Reasoning
by Hang Zhang, Wenxiao Zhang, Haoxuan Qu, Jun Liu
First submitted to arxiv on: 15 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
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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 paper proposes a novel framework, V-HOI Multi-LLMs Collaborated Reasoning (V-HOI MLCR), for Video-based Human-Object Interaction (V-HOI) detection in dynamic scenes. The goal is to enable mobile robots and autonomous driving systems to comprehensively understand human-object interactions and make informed decisions. Current V-HOI models have achieved high accuracy on specific datasets but lack the general reasoning ability of humans. To address this, the authors design a two-stage collaboration system comprising different off-the-shelf pre-trained large language models (LLMs). The first stage involves Cross-Agents Reasoning, where LLMs conduct reasoning from different aspects. In the second stage, Multi-LLMs Debate is used to obtain the final reasoning answer based on the knowledge in each LLM. Additionally, an auxiliary training strategy utilizing CLIP, a large vision-language model, enhances the base V-HOI models’ discriminative ability to cooperate with LLMs. The proposed framework improves the prediction accuracy of the base V-HOI model by reasoning from multiple perspectives. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about developing a new way for robots and self-driving cars to understand how humans interact with objects in videos. Current methods are good at detecting specific types of interactions, but they don’t have the same common sense as humans do. The authors propose a system that uses artificial intelligence models to reason together and make better decisions. This system is like having multiple experts discussing an issue and coming up with a conclusion based on their individual knowledge. By working together, these experts can improve the accuracy of detecting human-object interactions in videos. |
Keywords
» Artificial intelligence » Language model
