no code implementations • 24 Dec 2024 • Jinming Liu, Yuntao Wei, Junyan Lin, Shengyang Zhao, Heming Sun, Zhibo Chen, Wenjun Zeng, Xin Jin
While learned image compression methods have achieved impressive results in either human visual perception or machine vision tasks, they are often specialized only for one domain.
no code implementations • 16 Aug 2024 • Jinming Liu, Yuntao Wei, Junyan Lin, Shengyang Zhao, Heming Sun, Zhibo Chen, Wenjun Zeng, Xin Jin
We present a new image compression paradigm to achieve ``intelligently coding for machine'' by cleverly leveraging the common sense of Large Multimodal Models (LMMs).
no code implementations • 18 Apr 2024 • Qian Li, Cheng Ji, Shu Guo, Yong Zhao, Qianren Mao, Shangguang Wang, Yuntao Wei, JianXin Li
Existing methods are limited by their neglect of the multiple entity pairs in one sentence sharing very similar contextual information (ie, the same text and image), resulting in increased difficulty in the MMRE task.
no code implementations • 4 Jan 2024 • Yuntao Wei, Yuzhe Zhang, Shuyang Zhang, Hong Zhang
Multimodal depression detection is an important research topic that aims to predict human mental states using multimodal data.