Summary of Vg4d: Vision-language Model Goes 4d Video Recognition, by Zhichao Deng and Xiangtai Li and Xia Li and Yunhai Tong and Shen Zhao and Mengyuan Liu
VG4D: Vision-Language Model Goes 4D Video Recognition
by Zhichao Deng, Xiangtai Li, Xia Li, Yunhai Tong, Shen Zhao, Mengyuan Liu
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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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 proposes a novel framework called Vision-Language Models Goes 4D (VG4D) that leverages pre-trained Vision-Language Models (VLMs) to improve recognition performance in 4D point cloud video applications. The authors align the representation of a 4D encoder with a VLM, allowing for knowledge transfer from visual-text pre-training to 4D point clouds. This framework is demonstrated to achieve state-of-the-art performance on action recognition tasks using the NTU RGB+D 60 and 120 datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps robots and self-driving cars understand the world better by recognizing actions in 4D point cloud videos. It uses special AI models that learn from lots of images and text, then applies this knowledge to a new type of data called 4D point clouds. The result is a more accurate way to recognize actions like people walking or cars moving. |
Keywords
» Artificial intelligence » Encoder
