arXiv:2609.24215v1 Announce Type: new
Abstract: Although text-to-image models can accurately depict subjects and scenes, creators still struggle to specify the fine-grained emotions an image should c...
By Minglang Li, Yueyue Fang, Xieping Gao
arXiv:2505.24427v2 Announce Type: replace
Abstract: Accurate modeling of subjective phenomena such as emotion expression requires data annotated with authors' intentions. Commonly such data is collec...
By Christopher Bagdon, Aidan Combs, Carina Silberer, Roman Klinger
arXiv:2606. 05816v1 Announce Type: cross Abstract: T2I models cannot effectively capture sentiment from various types of text, including diaries, as they primarily focus on visual object-related patterns rather than contextual emotional understanding.
By Jihun Cho, Soo-Yeon Jeong, Sun-Young Ihm
The paper introduces CLEAR, a framework for Multimodal Emotion Recognition in Conversation that couples contextual representations with local discriminative cues. It replaces emotion label generation with continuous internal representations from a vision‑language model and trains a classifier on these representations. Sample‑adaptive residual coupling is used to integrate textual and acoustic evidence from the target utterance into the contextual representation, improving emotion recognition performance on IEMOCAP and MELD datasets.
By Linan Zhu, Zihao Zhai, Mengfei Xu, Xiao Han, Yuqian Fu, Xiangfan Chen, Zihang Zhang
arXiv:2609.26907v1 Announce Type: cross
Abstract: Memes often derive their harmful, hateful, or sarcastic meaning from small but decisive visual, textual, or cross-modal cues. Existing multimodal cla...
By Akshit Sharma, Prashant W. Patil
arXiv:2602. 16161v4 Announce Type: replace-cross Abstract: Emotional expression underpins natural communication and effective human-computer interaction.
By Rong Fu, Ziming Wang, Shuo Yin, Haiyun Wei, Kun Liu, Xianda Li, Simon Fong