arXiv:2607. 25679v1 Announce Type: cross Abstract: Multimodal behavioral analysis offers a scalable approach to assessing depression, anxiety, and stress, yet generic fusion models often ignore the psychometric structure of questionnaire labels.
By Shiyu Teng, Haichen Yu, Jiaqing Liu, Hao Sun, Yu Song, Shurong Chai, Ruibo Hou, Lanfen Lin, Yen-Wei Chen
arXiv:2608. 03611v1 Announce Type: new Abstract: Multimodal Sentiment Analysis (MSA) integrates text, audio, and vision to infer human affect, yet real-world multimodal observations are often incomplete.
By Chunlei Meng, Jacqueline J. Pang, Pengbin Feng, Zhenyu Yu, Chun Ouyang, Zhongxue Gan
arXiv:2501. 16106v2 Announce Type: replace Abstract: Recent advances in multimodal depression recognition for clinical interviews (MDRC) have demonstrated the potential of AI systems by integrating textual, acoustic, and facial cues.
By Wenjie Zheng, Qiming Xie, Jianfei Yu, Yang Wang, Lei Cao, Fei Wang, Shijin Wang, Rui Xia, Chengqing Zong
arXiv:2607. 21496v1 Announce Type: cross Abstract: Cognitive impairment (CI) is a growing public health concern.
By Yingchao Huang, Xin Wang, Yuhan Su, Shanshan Yao
arXiv:2608.21941v1 Announce Type: new
Abstract: Accurate assessment of patients in intensive care units (ICUs) is essential for timely clinical intervention and improved patient outcomes. Multimodal...
By Yixin Yang, Yueyang Sun, Weichen Liu, Xianbing Zhao, Sicen Liu
arXiv:2606. 06285v1 Announce Type: new Abstract: Time series foundation models (TS-FMs) aim to learn generalizable temporal representations that can be adapted to a wide range of downstream tasks.
By Ziwen Kan, Yishuo Chen, Kecheng Li, Andrew Wen, Xiaomeng Wang, Liwei Wang, Jihao Duan, Song Wang, Hongfang Liu, Tianlong Chen