arXiv:2606. 02679v1 Announce Type: new Abstract: Multimodal systems often benefit from combining information across language, sound, and visual streams, but this benefit is not guaranteed.
By Jiyuan Liu, Liangwei Nathan Zheng, Wei Emma Zhang, Xinpei Wang, Weitong Chen
arXiv:2607. 12774v1 Announce Type: cross Abstract: This article presents our results for the 11th Affective Behavior Analysis in-the-Wild (ABAW) competition.
By Aleksei Bakin, Andrey V. Savchenko
arXiv:2409. 00240v2 Announce Type: replace-cross Abstract: Automatic facial action unit (AU) recognition is used widely in facial expression analysis.
By Shuangquan Feng, Virginia R. de Sa
arXiv:2607. 08573v1 Announce Type: new Abstract: Multimodal emotion and sentiment recognition is commonly addressed by early fusion, which concatenates modalities before classification, or late fusion, which combines independently trained unimodal predictors.
By Adis Alihodzic, Selma Skopljakovic Hubljar
arXiv:2606. 11930v1 Announce Type: cross Abstract: Predicting psychological traits from asynchronous video interviews (AVIs) is a challenging multimodal learning problem because labeled datasets are limited while each response contains high-dimensional visual, acoustic, and verbal signals.
By Kuo-En Hung, Hung-Yue Suen, Shih-Ching Yeh, Hsiang-Wen Wang
arXiv:2606. 00170v1 Announce Type: cross Abstract: In recent years, emotion recognition based on physiological signals such as electroencephalogram (EEG) has gained considerable attention, as internal physiological data offer greater objectivity and reliability compared to external behavioral data like facial expressions.
By Zheng Wang, Shuo Wang, Junhong Wang