119 citations · 150 across the 6 of their papers we have counts for
6 papers
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…