Summary of Learning Truncated Causal History Model For Video Restoration, by Amirhosein Ghasemabadi et al.
Learning Truncated Causal History Model for Video Restoration
by Amirhosein Ghasemabadi, Muhammad Kamran Janjua, Mohammad Salameh, Di Niu
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 TURTLE model efficiently learns a truncated causal history for high-performing video restoration. Unlike traditional approaches, TURTLE stores and summarizes frame latent representations into an evolving historical state through a similarity-based retrieval mechanism. This enables recurrence in inference while allowing parallel training by sampling truncated video clips. The model achieves new state-of-the-art results on various video restoration tasks, including desnowing, deraining, super-resolution, deblurring, and denoising, with reduced computational cost compared to existing best contextual methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TURTLE is a new way to restore old or damaged videos. It works by remembering the past frames of a video and using that information to fix any problems in the current frame. This helps TURTLE learn how to remove snow, rain, and other distractions from videos more efficiently than other methods. The results are amazing, with TURTLE beating all the previous best methods for restoring videos on many different tasks. |
Keywords
» Artificial intelligence » Inference » Super resolution
