Summary of Rose: Revolutionizing Open-set Dense Segmentation with Patch-wise Perceptual Large Multimodal Model, by Kunyang Han et al.
ROSE: Revolutionizing Open-Set Dense Segmentation with Patch-Wise Perceptual Large Multimodal Model
by Kunyang Han, Yibo Hu, Mengxue Qu, Hailin Shi, Yao Zhao, Yunchao Wei
First submitted to arxiv on: 29 Nov 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 proposed Revolutionary Open-set dense SEgmentation LMM (ROSE) enables free-form category self-generation and open-category prediction by treating each image patch as an independent region of interest candidate, predicting both dense and sparse masks simultaneously. This is achieved through a newly designed instruction-response paradigm that leverages the generation capabilities of large multimodal models (LMMs). Additionally, a conversation-based refinement paradigm refines mask detail and category precision by integrating predictions with textual prompts. ROSE achieves competitive performance across various segmentation tasks in a unified framework. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ROSE is a new way to do image segmentation that lets computers generate categories on their own without needing specific instructions. This makes it possible to use the same system for different tasks, like labeling all the objects in an image or identifying what’s happening in a scene. The approach uses big models that can understand and work with lots of information from images, and it helps them predict where things are in the image by treating small parts of the image as separate regions to look at. This makes ROSE better at finding details and being more accurate. |
Keywords
» Artificial intelligence » Image segmentation » Mask » Precision
