no code implementations • 17 Feb 2024 • Jiateng Liu, Pengfei Yu, Yuji Zhang, Sha Li, Zixuan Zhang, Heng Ji
The dynamic nature of real-world information necessitates efficient knowledge editing (KE) in large language models (LLMs) for knowledge updating.
no code implementations • 1 Jan 2024 • Ke Yang, Jiateng Liu, John Wu, Chaoqi Yang, Yi R. Fung, Sha Li, Zixuan Huang, Xu Cao, Xingyao Wang, Yiquan Wang, Heng Ji, ChengXiang Zhai
The prominent large language models (LLMs) of today differ from past language models not only in size, but also in the fact that they are trained on a combination of natural language and formal language (code).
1 code implementation • 19 Sep 2023 • Xingyao Wang, Zihan Wang, Jiateng Liu, Yangyi Chen, Lifan Yuan, Hao Peng, Heng Ji
However, current evaluation protocols often emphasize benchmark performance with single-turn exchanges, neglecting the nuanced interactions among the user, LLMs, and external tools, while also underestimating the importance of natural language feedback from users.
no code implementations • 21 Mar 2023 • Colton Stearns, Davis Rempe, Jiateng Liu, Alex Fu, Sebastien Mascha, Jeong Joon Park, Despoina Paschalidou, Leonidas J. Guibas
Modern depth sensors such as LiDAR operate by sweeping laser-beams across the scene, resulting in a point cloud with notable 1D curve-like structures.
1 code implementation • 30 Nov 2021 • Xingxun Jiang, Yuan Zong, Wenming Zheng, Jiateng Liu, Mengting Wei
To solve these problems, this paper proposes a novel Transfer Group Sparse Regression method, namely TGSR, which aims to 1) optimize the measurement and better alleviate the difference between the source and target databases, and 2) highlight the valid facial regions to enhance extracted features, by the operation of selecting the group features from the raw face feature, where each region is associated with a group of raw face feature, i. e., the salient facial region selection.
no code implementations • 19 Oct 2020 • Jiateng Liu, Wenming Zheng, Yuan Zong
Correctly perceiving micro-expression is difficult since micro-expression is an involuntary, repressed, and subtle facial expression, and efficiently revealing the subtle movement changes and capturing the significant segments in a micro-expression sequence is the key to micro-expression recognition (MER).
no code implementations • 13 Aug 2020 • Xingxun Jiang, Yuan Zong, Wenming Zheng, Chuangao Tang, Wanchuang Xia, Cheng Lu, Jiateng Liu
Experimental results show that DFEW is a well-designed and challenging database, and the proposed EC-STFL can promisingly improve the performance of existing spatiotemporal deep neural networks in coping with the problem of dynamic FER in the wild.
Ranked #17 on Dynamic Facial Expression Recognition on DFEW
Dynamic Facial Expression Recognition Facial Expression Recognition +1