arXiv:2607. 12787v1 Announce Type: new Abstract: Recent advances in multimodal large language models (MLLMs) have significantly improved the performance of multimodal emotion recognition (MER) and enabled interpretable description generation by jointly modeling video, audio, and language, etc.
By Kaiwen Zheng, Junchen Fu, Wenhao Deng, Hu Han, Joemon M. Jose, Xuri Ge
Recent advances in multimodal large language models (MLLMs) have significantly improved the performance of multimodal emotion recognition (MER) and enabled interpretable description generation by jointly modeling video, audio, and language, etc. However, these performance improvements are often accompanied by an increase in model parameter size (e.
arXiv:2608. 10448v1 Announce Type: new Abstract: Multimodal emotion recognition in conversation (MERC) requires understanding complex interactions between verbal and non-verbal cues.
By Sujung Oh, Jung Uk Kim, Sangmin Lee
The paper proposes an agentic framework for multimodal emotion reasoning that uses frozen models to avoid costly instruction tuning. It employs modality‑specific perception agents, an integration agent, and an MLLM critic to generate emotion predictions with evidence citations, followed by an evidence‑grounded verification stage that identifies errors and applies targeted prompt patches without updating model weights. On the MER2025 dataset, this approach improves F‑score across several large language‑vision models, outperforming static pipelines and matching or exceeding reflection baselines.
By Elyas Rashno, Farhana Zulkernine
arXiv:2607. 21013v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have achieved impressive performance in multimodal emotion recognition (MER) tasks and lifted MER to a new level that is complex emotion understanding with advanced video understanding abilities and natural language description.
By Lihuang Fang, Yuchen Zou, kebin Jin, Jinghui Qin
The paper introduces a new paradigm for multimodal emotion understanding that moves beyond surface-level cue-label associations by incorporating cognitive appraisal reasoning. It presents CogEmo-40K, a large instruction‑tuning dataset that elicits evidence‑grounded reasoning across six appraisal dimensions; CogEmo‑MoE, a compact sparse multimodal large language model with interleaved MoE blocks for appraisal‑specific adaptation; and CogEmo‑Bench, a benchmark featuring an Appraisal Evidence Quality Score (AEQS) to evaluate why emotions arise. Experiments demonstrate that this perception‑to‑appraisal approach improves cross‑domain generalization and aligns model reasoning more closely with human cognitive processes.
By Jia Li, Yichao He, Yangchen Yu, Qiankun Li, Xinyi Li, Baiyi Ye, Zhenzhen Hu, Richang Hong, Erik Cambria