arXiv:2608. 04054v1 Announce Type: cross Abstract: Multimodal intent recognition requires understanding not only what textual, acoustic, and visual signals share, but also how they disagree.
By Mohnish Raj, Suraj Kumar, Soumi Chattopadhayay, Chandranath Adak, Ayan Dutta
arXiv:2607. 18336v1 Announce Type: cross Abstract: Multimodal emotion recognition in conversation (MERC) can leverage multimodal and contextual cues to boost recognition performance.
By Zilong Huang, Kong Aik Lee, Junjie Li, Zhe Li, Man-Wai Mak
arXiv:2608. 04013v1 Announce Type: cross Abstract: Recent advances in Multimodal Emotion Recognition in Conversations (MERC) highlight its reliance on complete multimodal inputs.
By Yuntao Shou, Tao Meng, Wei Ai, Keqin Li
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:2608. 04509v1 Announce Type: new Abstract: Vision-language systems combine images with retrieved text, but these sources can disagree or jointly fail to support an answer.
By De Jiang, Zhengyang Zhang, Kehong Yuan, Shaohua Ma
arXiv:2608. 03475v1 Announce Type: cross Abstract: Multimodal intent recognition combines linguistic, acoustic, and visual evidence, but individual modalities may be noisy, missing, semantically conflicting, or disproportionately dominant.
By Suraj Kumar, Mohnish Raj, Soumi Chattopadhayay, Chandranath Adak, Ayan Dutta