no code implementations • EMNLP 2021 • Xiaobao Guo, Adams Kong, Huan Zhou, Xianfeng Wang, Min Wang
Specifically, to improve unimodal representations, a unimodal refinement module is designed to refine modality-specific learning via iteratively updating the distribution with transformer-based attention layers.
no code implementations • 14 Jan 2024 • Fan Zhang, Xiaobao Guo, Xiaojiang Peng, Alex Kot
In addition, when compared with the domain disparity existing between face datasets and FER datasets, the divergence between general datasets and FER datasets is more pronounced.
no code implementations • 20 Mar 2023 • Ruochen Zhao, Hailin Chen, Weishi Wang, Fangkai Jiao, Xuan Long Do, Chengwei Qin, Bosheng Ding, Xiaobao Guo, Minzhi Li, Xingxuan Li, Shafiq Joty
As Large Language Models (LLMs) become popular, there emerged an important trend of using multimodality to augment the LLMs' generation ability, which enables LLMs to better interact with the world.
1 code implementation • ICCV 2023 • Xiaobao Guo, Nithish Muthuchamy Selvaraj, Zitong Yu, Adams Wai-Kin Kong, Bingquan Shen, Alex Kot
Despite this, deception detection research is hindered by the lack of high-quality deception datasets, as well as the difficulties of learning multimodal features effectively.
no code implementations • 11 Feb 2023 • Zhaoxu Li, Zitong Yu, Nithish Muthuchamy Selvaraj, Xiaobao Guo, Bingquan Shen, Adams Wai-Kin Kong, Alex Kot
Detecting deception by human behaviors is vital in many fields such as custom security and multimedia anti-fraud.
3 code implementations • 12 Mar 2020 • Han Yang, Ruimao Zhang, Xiaobao Guo, Wei Liu, WangMeng Zuo, Ping Luo
First, a semantic layout generation module utilizes semantic segmentation of the reference image to progressively predict the desired semantic layout after try-on.
Ranked #4 on Virtual Try-on on VITON (IS metric)