Summary of Raformer: Redundancy-aware Transformer For Video Wire Inpainting, by Zhong Ji et al.
Raformer: Redundancy-Aware Transformer for Video Wire Inpainting
by Zhong Ji, Yimu Su, Yan Zhang, Jiacheng Hou, Yanwei Pang, Jungong Han
First submitted to arxiv on: 24 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 introduces a novel application in video inpainting called Video Wire Inpainting (VWI), which aims to remove wires from films or TV series efficiently. The existing wire removal datasets are limited by their small size, poor quality, and limited variety of scenes. To address this, the authors propose a new dataset, Wire Removal Video Dataset 2 (WRV2), with a novel mask generation strategy called Pseudo Wire-Shaped (PWS) Masks. The WRV2 dataset contains over 4,000 videos with an average length of 80 frames, designed to facilitate the development and evaluation of inpainting models. The paper also proposes the Redundancy-Aware Transformer (Raformer) method that addresses the unique challenges of wire removal in video inpainting. Unlike conventional approaches, Raformer employs a novel strategy to selectively bypass redundant parts, such as static background segments devoid of valuable information for inpainting. The core of Raformer is the Redundancy-Aware Attention (RAA) module, which isolates and accentuates essential content through a coarse-grained, window-based attention mechanism. The authors conduct extensive experiments on both traditional video inpainting datasets and the proposed WRV2 dataset, demonstrating that Raformer outperforms other state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Removing wires from old movies can be a time-consuming task. Researchers have developed a new method to make this process more efficient. They created a large dataset of videos with wires removed, which will help develop better algorithms for removing wires in the future. The new algorithm, called Raformer, is designed specifically for removing wires and outperforms other methods. |
Keywords
» Artificial intelligence » Attention » Mask » Transformer
