Summary of Adair: Exploiting Underlying Similarities Of Image Restoration Tasks with Adapters, by Hao-wei Chen et al.
AdaIR: Exploiting Underlying Similarities of Image Restoration Tasks with Adapters
by Hao-Wei Chen, Yu-Syuan Xu, Kelvin C.K. Chan, Hsien-Kai Kuo, Chun-Yi Lee, Ming-Hsuan Yang
First submitted to arxiv on: 17 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 The paper presents a novel framework, called AdaIR, for efficient image restoration tasks. Unlike existing methods that rely on extensive networks specifically designed for individual degradations, AdaIR exploits the commonalities among restoration tasks to reduce storage costs and computational overheads. The approach involves self-supervised pre-training of a generic restoration network using synthetic degradations, followed by training lightweight adapters to adapt the pre-trained network to specific degradations. This framework requires minimal additional parameters (1.9 MB) and training time (7 hours) per task, making it an efficient solution for multi-task image restoration. The paper provides extensive experimental results demonstrating the effectiveness of AdaIR, showcasing its potential in various applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper introduces a new way to restore damaged images without using lots of storage space or computer power. Usually, image restoration methods require large networks specifically designed for each type of damage. This approach can be inefficient and slow. The authors propose a different method called AdaIR that shares common parts among different restoration tasks. This makes the process more efficient by requiring less storage and training time. The paper shows that AdaIR works well in various situations, using much fewer resources than existing methods. |
Keywords
» Artificial intelligence » Multi task » Self supervised
