VISTA (Value-Informed Semantic Trust Arbitration) is a learned seven-field appraisal interface that conditions modality arbitration on concerns, event relations, and expression conditions while retaining a joint-evidence residual. It uses a log-odds decomposition to separate emotion expectation from cue diagnosticity, allowing appraisal to change how evidence is interpreted. With a shared Qwen2.5-Omni-7B backbone, VISTA achieves 64.5% conflict accuracy on CA-MER, improving on modality gating by 2.5 percentage points on conflict and 0.2 on consistency, and a frozen-backbone probe reaches 0.600 macro CCC for appraisal readout versus 0.505 for emotion-only fine-tuning.
By Jiale Dai, Liuxian Ma, Xiaoke Niu, Wenjing Zhang, Huiying Zhao, Zhaoxiang Liu, Shiguo Lian, Guojie Song
BiCFlow-MER introduces a conditional-flow framework for audio-text multimodal emotion recognition, treating the task as generative evidence transport within a structured emotion space. It disentangles emotion-oriented evidence from speaker style and lexical content, creating a conflict-aware affective condition that guides bidirectional rectified flow to an explicit emotion-space endpoint. The model verifies candidate emotions via adaptive prototype-cloud scoring and backward class-to-condition consistency, achieving superior performance on IEMOCAP, MELD, and the zero-shot CASE benchmark.
By Yanbing Wang, Shenyue Wang, Chunyang Yu
In multimodal emotion recognition (MER), human affective states are inferred by integrating complementary cues from multiple modalities. In audio-text MER, affective cues are often entangled with spea...
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
ReH-FUSE is a reliability‑aware hierarchical fusion framework for multimodal emotion recognition in conversation. It uses a decision‑level router to first compare the relative preference between text and audio, then balances this unimodal mixture with a cross‑modal expert, thereby separating unimodal competition from cross‑modal selection. Experiments on IEMOCAP and MELD show that ReH-FUSE achieves state‑of‑the‑art weighted and macro F1 scores, and ablation studies confirm that learned routing outperforms uniform expert averaging and benefits from cross‑modal interaction.
By Guan-Hua Wen, Hou-Chiang Tseng, Kuan-Yu Chen