Summary of Structured Click Control in Transformer-based Interactive Segmentation, by Long Xu and Yongquan Chen and Rui Huang and Feng Wu and Shiwu Lai
Structured Click Control in Transformer-based Interactive Segmentation
by Long Xu, Yongquan Chen, Rui Huang, Feng Wu, Shiwu Lai
First submitted to arxiv on: 7 May 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 paper proposes a structured click intent model based on graph neural networks to improve the robustness of interactive segmentation in transformer-based models. The approach adaptively obtains graph nodes via global similarity of user-clicked tokens, aggregates them into interaction features, and injects these features into vision transformer features using dual cross-attention. This allows for better control over segmentation results. The proposed algorithm is demonstrated to be effective in improving performance in transformer-based interactive segmentation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to make click-point-based interactive segmentation more reliable by creating a special model that looks at how users are clicking on an image. It does this by using something called graph neural networks, which can learn patterns in the data. The model takes the clicks and turns them into features that can be used to improve the segmentation results. This means that the computer will do a better job of dividing the image into meaningful parts based on how users are interacting with it. |
Keywords
» Artificial intelligence » Cross attention » Transformer » Vision transformer
