Summary of Diffcp: Ultra-low Bit Collaborative Perception Via Diffusion Model, by Ruiqing Mao et al.
DiffCP: Ultra-Low Bit Collaborative Perception via Diffusion Model
by Ruiqing Mao, Haotian Wu, Yukuan Jia, Zhaojun Nan, Yuxuan Sun, Sheng Zhou, Deniz Gündüz, Zhisheng Niu
First submitted to arxiv on: 29 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Multiagent Systems (cs.MA)
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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 A novel collaborative perception paradigm called DiffCP is proposed to efficiently compress sensing information of collaborators, enabling feature-level collaboration with ultra-low communication cost. This approach utilizes a specialized diffusion model that incorporates geometric and semantic conditions to reduce bandwidth demands. The system can be seamlessly integrated into existing CP algorithms to enhance various downstream tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a team of robots working together to build something complex. They need to share information about what they’re seeing, but it’s hard for them to send all the details over the airwaves without getting overwhelmed. A new way of doing this called DiffCP solves this problem by using a special kind of math to squeeze down the information into a tiny package that can be sent quickly and easily. This lets the robots work together better and get more done. |
Keywords
* Artificial intelligence * Diffusion model
