Summary of A Survey on Backbones For Deep Video Action Recognition, by Zixuan Tang et al.
A Survey on Backbones for Deep Video Action Recognition
by Zixuan Tang, Youjun Zhao, Yuhang Wen, Mengyuan Liu
First submitted to arxiv on: 9 May 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 Action recognition plays a crucial role in developing interactive metaverses, where deep learning has significantly advanced related methods. Researchers have designed various backbones, resulting in diverse approaches and challenges. This paper reviews several action recognition techniques based on deep neural networks, categorized into three parts: Two-Streams networks and their variants, which utilize RGB video frames and optical flow modalities; 3D convolutional networks that directly process RGB data to extract motion information; and Transformer-based methods that incorporate natural language processing models into computer vision and video understanding. The review provides an objective perspective on these techniques and serves as a reference for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about recognizing actions in videos, which is important for building virtual worlds where people can interact with each other. Researchers have developed different ways to do this using artificial intelligence. This paper reviews three main approaches: one uses two types of video frames and motion patterns; another uses 3D computer vision to analyze movement; and the third uses language processing models to understand actions in videos. The review aims to provide a clear understanding of these methods and help guide future research. |
Keywords
» Artificial intelligence » Deep learning » Natural language processing » Optical flow » Transformer
