arXiv Computer Vision
AffectReveal: Event-Grounded Emotion Recognition Beyond Visual Appearances
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv AI
Jul 22
Why Do Vision Language Models Struggle To Recognize Human Emotions?
arXiv:2604. 15280v2 Announce Type: replace-cross Abstract: Understanding emotions is a fundamental ability for intelligent systems to be able to interact with humans.
arXiv Computer Vision
4d ago
Decoding Affective Nuances: Enhancing MLLMs via Hierarchical Emotion Reasoning and Contrastive Discriminative Pruning
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.
arXiv Computer Vision
4d ago
Causal-EVC: Breaking Emotional Spurious Causality via Spatiotemporal Grounding and Counterfactual Intervention
arXiv:2609.36776v1 Announce Type: new Abstract: Emotional Video Captioning aims to generate factually accurate and emotionally empathetic descriptions. While recent methods have recognized the import...
arXiv AI
4d ago
VISTA: Value-Informed Event Appraisal for Multimodal Emotion Conflict
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.
arXiv AI
Aug 12
Rationale-Guided Learning for Multimodal Emotion Recognition
arXiv:2608. 10448v1 Announce Type: new Abstract: Multimodal emotion recognition in conversation (MERC) requires understanding complex interactions between verbal and non-verbal cues.
arXiv AI
Jun 11
Causal Emotion Recognition in Conversation: Context Saturation and Discourse-Marker Evidence
arXiv:2601. 00181v3 Announce Type: replace-cross Abstract: We address two persistent gaps in Emotion Recognition in Conversation: which modeling choices materially affect performance, and how recognition findings connect to interpretable discourse-level patterns.