Summary of Efficient Learnable Collaborative Attention For Single Image Super-resolution, by Yigang Zhao Chaowei Zheng et al.
Efficient Learnable Collaborative Attention for Single Image Super-Resolution
by Yigang Zhao Chaowei Zheng, Jiannan Su, GuangyongChen, MinGan
First submitted to arxiv on: 7 Apr 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 presents a novel technique for image super-resolution called Learnable Collaborative Attention (LCoA). The proposed LCoA aims to address the high computational complexity and memory consumption of Non-Local Attention (NLA) by introducing inductive bias into non-local modeling. The LCoA consists of two components: Learnable Sparse Pattern (LSP) and Collaborative Attention (CoA). LSP uses k-means clustering to dynamically adjust the sparse attention pattern, reducing the number of non-local modeling rounds. CoA leverages the sparse attention pattern and weights learned by LSP, co-optimizing the similarity matrix across different abstraction levels. The experimental results demonstrate that LCoA can reduce non-local modeling time by about 83% in the inference stage. Furthermore, the authors integrate LCoA into a deep Learnable Collaborative Attention Network (LCoAN), achieving competitive performance in terms of inference time, memory consumption, and reconstruction quality compared to state-of-the-art SR methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to zoom in on a blurry picture. This paper develops a new way to make that picture clearer by using attention. Attention helps focus on important parts of the image. The new method is called Learnable Collaborative Attention (LCoA). LCoA makes it faster and more efficient to use attention for super-resolution tasks. It does this by grouping similar features together, reducing the amount of calculations needed. The results show that LCoA can make images clearer and faster than other methods. This new approach could be used in many applications, such as enhancing security cameras or improving medical imaging technology. |
Keywords
» Artificial intelligence » Attention » Clustering » Inference » K means » Super resolution
