119 citations · 161 across the 8 of their papers we have counts for
8 papers
Position-Enhanced Visual Instruction Tuning for Multimodal Large Language Models
Chi Chen, Ruoyu Qin, Fuwen Luo +4
Recently, Multimodal Large Language Models (MLLMs) that enable Large Language Models (LLMs) to interpret images through visual instruction tuning have achieved significant success.…
Improving Fake News Detection by Using an Entity-enhanced Framework to Fuse Diverse Multimodal Clues
Peng Qi, Juan Cao, Xirong Li +7
Recently, fake news with text and images have achieved more effective diffusion than text-only fake news, raising a severe issue of multimodal fake news detection. Current studies…
Multi-modal Affect Analysis using standardized data within subjects in the Wild
Sachihiro Youoku, Takahisa Yamamoto, Junya Saito +7
Human affective recognition is an important factor in human-computer interaction. However, the method development with in-the-wild data is not yet accurate enough for practical usa…
Action Units Recognition Using Improved Pairwise Deep Architecture
Junya Saito, Xiaoyu Mi, Akiyoshi Uchida +4
Facial Action Units (AUs) represent a set of facial muscular activities and various combinations of AUs can represent a wide range of emotions. AU recognition is often used in many…
Progressive Domain Expansion Network for Single Domain Generalization
Lei Li, Ke Gao, Juan Cao +6
Single domain generalization is a challenging case of model generalization, where the models are trained on a single domain and tested on other unseen domains. A promising solution…
Action Units Recognition by Pairwise Deep Architecture
Junya Saito, Ryosuke Kawamura, Akiyoshi Uchida +5
In this paper, we propose a new automatic Action Units (AUs) recognition method used in a competition, Affective Behavior Analysis in-the-wild (ABAW). Our method tackles a problem…
A Multi-term and Multi-task Analyzing Framework for Affective Analysis in-the-wild
Sachihiro Youoku, Yuushi Toyoda, Takahisa Yamamoto +4
Human affective recognition is an important factor in human-computer interaction. However, the method development with in-the-wild data is not yet accurate enough for practical usa…
HiCOMEX: Facial Action Unit Recognition Based on Hierarchy Intensity Distribution and COMEX Relation Learning
Ziqiang Shi, Liu Liu, Zhongling Liu +3
The detection of facial action units (AUs) has been studied as it has the competition due to the wide-ranging applications thereof. In this paper, we propose a novel framework for…