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arXiv:2607. 18336v1 Announce Type: cross Abstract: Multimodal emotion recognition in conversation (MERC) can leverage multimodal and contextual cues to boost recognition performance.
The paper introduces a multimodal emotion recognition framework that combines audio and visual feature extraction with an attention-based fusion strategy. Audio features include Wav2Vec2 embeddings, MFCCs, and statistical acoustic descriptors, fused via a BiLSTM, while video features are extracted using a ResNet50-BiLSTM architecture. A multi-head attention mechanism fuses these modalities, and experiments on MELD and IEMOCAP show significant accuracy and robustness gains, especially in unbalanced data settings.
arXiv:2608.20905v1 Announce Type: new Abstract: Face-to-face audiovisual interaction is central to human communication, conveying rich emotional and social cues. However, existing multimodal dialogue...
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.
The paper introduces Emo-DVS, a large-scale, multimodal dataset combining event camera, audio, and text data for emotion recognition, designed to mitigate privacy concerns associated with RGB cameras. It proposes the Information‑Guided Gated Fusion (IGF) framework, which pre‑trains an event encoder on the dataset’s FAU subset, adaptively gates modalities to reduce noise, and aligns cross‑modal representations via mutual information maximization. Experiments show that IGF outperforms existing methods on this challenging tri‑modal benchmark.
DiaRelay introduces a lightweight adapter that lets large language models maintain a constant‑size dialogue‑level memory for emotion recognition in conversation. It builds on LoRA by adding a Selective Relay Memory Transition that aggregates useful historical evidence into a bounded memory, and a Dual‑axis Relay Memory Read that uses this memory to modulate low‑rank feature transformations. Experiments show DiaRelay achieves state‑of‑the‑art weighted F1 and accuracy on MELD with only 7.1 M additional trainable parameters, while also performing competitively on IEMOCAP.