A Smarter Way for AI to Understand Videos
orig. “Native Active Perception as Reasoning for Omni-Modal Understanding” · Zhenghao Xing, Ruiyang Xu, Yuxuan Wang, Jinzheng He, Ziyang Ma, Qize Yang, Yunfei Chu, Jin Xu, Junyang Lin, Chi-Wing Fu, Pheng-Ann Heng
When AI tries to understand a video, it usually has to look at every frame, which can be time-consuming and inefficient.
This is because traditional AI models are passive, meaning they process all the information in a video without deciding what's important.
A new approach, called active perception, allows AI to be more interactive and focus on the most relevant parts of the video.
This is done by using a cycle of observation, thought, and action, where the AI decides what to look at next and what to ignore
This new approach could make it possible for AI to understand videos more efficiently, which could be useful for things like video search, recommendation systems, and accessibility tools for people with disabilities.
It could also help reduce the amount of energy and computing power needed to process videos, which could be better for the environment
Zhenghao Xing, Ruiyang Xu, Yuxuan Wang, Jinzheng He, Ziyang Ma, Qize Yang, Yunfei Chu, Jin Xu, Junyang Lin, Chi-Wing Fu, Pheng-Ann Heng
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