arXiv:2604. 15280v2 Announce Type: replace-cross Abstract: Understanding emotions is a fundamental ability for intelligent systems to be able to interact with humans.
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: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...
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:2608. 10448v1 Announce Type: new Abstract: Multimodal emotion recognition in conversation (MERC) requires understanding complex interactions between verbal and non-verbal cues.
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.
Video2Reaction is a multimodal dataset that links short movie segments to the emotional reactions of viewers, gathered from social media comments. The dataset models reactions as distributions over categorical emotions, capturing the subjective and ambiguous nature of emotional perception. Experiments show that vision‑language models fine‑tuned with LoRA learn effectively from Video2Reaction and outperform specialized baselines, and that models pre‑fine‑tuned on this dataset transfer well to other emotion prediction tasks.
AffectDelta is a new image editing framework that moves beyond single emotion labels by modeling edits as transitions between eight‑dimensional emotion distributions. It uses a frozen Emotion Distribution Predictor to estimate the source state and a signed difference vector to encode the desired change, which is then translated into context‑dependent semantic and appearance modifications via a transition encoder and a diffusion backbone. The authors introduce AffectPair‑249K, a dataset of 248,841 source‑target pairs covering both cross‑category and within‑category transitions, and show that AffectDelta outperforms six baselines in affective alignment and content preservation.
arXiv:2609.05806v1 Announce Type: new Abstract: Emotion Recognition in Conversations (ERC) aims to identify speakers' emotions in multi-turn dialogue. Accurate emotion recognition can support a wide...
arXiv:2603. 03989v2 Announce Type: replace-cross Abstract: When visual evidence is ambiguous, vision models must decide how to interpret face-like patterns.
Understanding both expressed and evoked emotions is critical for multimodal large language models (MLLMs) to achieve comprehensive affect-aware interactions. However, existing benchmarks typically examine expressed and evoked emotions in isolation or are constrained to coarse-grained and incomplete affective characterizations.
arXiv:2607. 02089v1 Announce Type: cross Abstract: Vision-language models (VLMs) have achieved strong performance across diverse multimodal tasks, yet they remain vulnerable to unreliable reasoning.