Summary of Remamber: Referring Image Segmentation with Mamba Twister, by Yuhuan Yang et al.
ReMamber: Referring Image Segmentation with Mamba Twister
by Yuhuan Yang, Chaofan Ma, Jiangchao Yao, Zhun Zhong, Ya Zhang, Yanfeng Wang
First submitted to arxiv on: 26 Mar 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 This paper proposes a novel Referring Image Segmentation (RIS) architecture called ReMamber, which integrates the power of Mamba with a multi-modal Mamba Twister block. Mamba is an efficient linear complexity processing method that has achieved great success in capturing long-range visual-language dependencies. However, its quadratic computation cost makes it resource-consuming. To address this, ReMamber explicitly models image-text interaction and fuses textual and visual features through its unique channel and spatial twisting mechanism. The architecture achieves competitive results on three challenging benchmarks with a simple and efficient design. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ReMamber is a new way to understand complex images and text together. It uses an existing method called Mamba, which is fast but not perfect for long-range connections. To make it better, ReMamber adds a special block that helps image and text features work together. This makes it good at understanding images and text in combination. |
Keywords
* Artificial intelligence * Image segmentation * Multi modal
