Summary of Llm-seg: Bridging Image Segmentation and Large Language Model Reasoning, by Junchi Wang et al.
LLM-Seg: Bridging Image Segmentation and Large Language Model Reasoning
by Junchi Wang, Lei Ke
First submitted to arxiv on: 12 Apr 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 proposes a novel task called reasoning segmentation, which enables image segmentation systems to reason and interpret implicit user intention via large language model (LLM) reasoning. The authors develop a new framework called LLM-Seg that connects the foundational Segmentation Anything Model with an LLM by mask proposals selection. They also propose an automatic data generation pipeline and construct a new reasoning segmentation dataset, LLM-Seg40K, which serves as a benchmark for training and evaluating various approaches. Experiments demonstrate competitive performance of LLM-Seg compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about helping computers understand what we want them to do when we give them instructions with pictures. It’s like when you ask Siri or Alexa to show you a specific object, but instead of just showing it, the computer also tries to figure out why you wanted that object. The authors created a new way for computers to do this called reasoning segmentation. They also made a special dataset and model that can help other researchers develop their own reasoning segmentation systems. |
Keywords
» Artificial intelligence » Image segmentation » Large language model » Mask
