Summary of Segllm: Multi-round Reasoning Segmentation, by Xudong Wang et al.
SegLLM: Multi-round Reasoning Segmentation
by XuDong Wang, Shaolun Zhang, Shufan Li, Konstantinos Kallidromitis, Kehan Li, Yusuke Kato, Kazuki Kozuka, Trevor Darrell
First submitted to arxiv on: 24 Oct 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 SegLLM model is a novel approach to multi-round interactive reasoning segmentation that builds upon LLM-based segmentation by incorporating conversational memory of both visual and textual outputs. This allows the model to reason about complex user intentions, segment objects in relation to previously identified entities, and respond to queries in a chat-like manner. The model outperforms existing methods on the MRSeg benchmark by over 20%, and training on multi-round reasoning segmentation data also improves performance on standard single-round referring segmentation and localization tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The SegLLM model is a new way of doing object segmentation that uses artificial intelligence to understand conversations. It’s like having a chat with someone, where you can give them instructions and ask questions, and they can respond accordingly. This model is better than others at understanding what’s being asked and figuring out what objects are in a picture. It even gets better at this when it’s trained on more complex tasks. |
