arXiv:2602. 16161v4 Announce Type: replace-cross Abstract: Emotional expression underpins natural communication and effective human-computer interaction.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
Cross-Modal Emotion Understanding: A Transformer-GAT Approach for Dialogue Emotion Recognition proposes a hybrid framework that combines a Transformer and a Graph Attention Network to capture both global semantic information and fine-grained relationships between modalities. The model is evaluated on the IEMOCAP and MELD datasets, achieving weighted F1 scores of 72.45% and 77.37%, respectively, and surpasses state‑of‑the‑art methods. These results suggest that integrating multimodal features with balanced global and local context modeling can provide deeper emotional insights for dialogue emotion recognition.
arXiv:2601. 07565v2 Announce Type: replace-cross Abstract: Multimodal emotion understanding requires the integration of heterogeneous data sources, including text, audio, and visual modalities, while simultaneously addressing discrete emotion recognition and continuous sentiment analysis.
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.
arXiv:2608.30726v1 Announce Type: new Abstract: Multimodal Sentiment Analysis (MSA) is a fundamental component of affective computing that aims to decipher complex emotional states by integrating ver...
arXiv:2609.09924v1 Announce Type: new Abstract: Emotion Recognition in Conversations (ERC) requires integrating heterogeneous textual, audio, and visual cues while accounting for conversational conte...
The paper introduces DAN, a training‑free inference‑time framework that improves affective reasoning in multimodal large language models. It combines a Hierarchical Emotional Reasoning Chain (HERC) to better capture fine‑grained visual cues and a Contrastive Discriminative Visual Pruning (CDVP) module to isolate discriminative tokens for semantically similar emotions. Experiments show significant gains, notably a +10.47% improvement on the WebEmo25 benchmark with Qwen3‑VL‑8B‑Instruct.