Summary of Video Rwkv:video Action Recognition Based Rwkv, by Zhuowen Yin et al.
Video RWKV:Video Action Recognition Based RWKV
by Zhuowen Yin, Chengru Li, Xingbo Dong
First submitted to arxiv on: 8 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 This paper introduces the LCR (LSTM CrossRWKV) framework, a novel approach to video understanding that addresses challenges of high computational costs and long-distance dependencies. The proposed method incorporates a Cross RWKV gate to facilitate interaction between current frame edge information and past features, enhancing focus on the subject through edge features and globally aggregating inter-frame features over time. LCR stores long-term memory for video processing through an enhanced LSTM recurrent execution mechanism. By leveraging the Cross RWKV gate and recurrent execution, LCR effectively captures both spatial and temporal features. The paper sets a new benchmark in video understanding, offering a scalable and efficient solution for comprehensive video analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to understand videos using a special kind of computer model called LCR. Right now, it’s hard to do this because computers need to process lots of information quickly, but LCR makes it easier by learning from the edges of each frame and what happened in previous frames. It also helps the computer remember important things from the past. This new approach is really good at understanding videos and could be used for all sorts of things like analyzing sports games or recognizing animals. |
Keywords
» Artificial intelligence » Lstm
