Summary of Planllm: Video Procedure Planning with Refinable Large Language Models, by Dejie Yang and Zijing Zhao and Yang Liu
PlanLLM: Video Procedure Planning with Refinable Large Language Models
by Dejie Yang, Zijing Zhao, Yang Liu
First submitted to arxiv on: 26 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); 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 The proposed framework, PlanLLM, is a cross-modal joint learning approach that utilizes Large Language Models (LLMs) for video procedure planning. The method combines an LLM-Enhanced Planning module with Mutual Information Maximization to generate free-form planning outputs and enhance action step decoding. This allows the model to generalize to new steps or tasks and improve its reasoning ability. PlanLLM achieves superior performance on three benchmarks, demonstrating its effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PlanLLM is a new way for computers to plan out actions based on videos of starting and ending states. It uses big language models (like those used in chatbots) to help plan the steps needed to get from one state to another. The model can think about many different possible actions and choose the best ones. It also helps remove noise that might be present in specific situations. This means it can be used for a wide range of tasks, not just ones where the same steps always work. |
