AffectVerse improves multimodal emotion recognition by at least 2.57% on nine benchmarks through an Emotion World Module that performs short-horizon latent affective prediction via cross-modal temporal imagination and belief aggregation.
Affectgpt-r1: Leveraging reinforcement learning for open-vocabulary multimodal emotion recognition
6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 6representative citing papers
MER-R1 uses dual-objective RL to optimize fast-thinking recall and slow-thinking precision separately in multimodal emotion recognition, with calibration to align them, yielding SOTA results on two benchmarks.
A cue-coverage reward plus a modality-token KL penalty makes multimodal emotion-reasoning models cite more real visual/audio evidence and hallucinate less, with reported SoTA on emotion benchmarks.
Proposes AGSR and the FAB-G supervised multi-agent framework that predicts attribute salience from human annotations to constrain MLLM emotion reasoning, yielding gains on EmoArt and cross-dataset tests.
C2F-Thinker combines structured coarse-to-fine chain-of-thought reasoning with hint-guided GRPO reinforcement learning to achieve competitive fine-grained sentiment regression and superior cross-domain generalization in multimodal analysis.
MER2026 defines four tracks to advance generative emotion understanding from individual basic labels to dyadic, fine-grained, preference, and physiological scenarios.
citing papers explorer
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AffectVerse: Emotional World Models for Multimodal Affective Computing
AffectVerse improves multimodal emotion recognition by at least 2.57% on nine benchmarks through an Emotion World Module that performs short-horizon latent affective prediction via cross-modal temporal imagination and belief aggregation.
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MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy
MER-R1 uses dual-objective RL to optimize fast-thinking recall and slow-thinking precision separately in multimodal emotion recognition, with calibration to align them, yielding SOTA results on two benchmarks.
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Omni-Perception Policy Optimization for Multimodal Emotion Reasoning
A cue-coverage reward plus a modality-token KL penalty makes multimodal emotion-reasoning models cite more real visual/audio evidence and hallucinate less, with reported SoTA on emotion benchmarks.
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Attribute-Grounded Selective Reasoning for Artwork Emotion Understanding with Multimodal Large Language Models
Proposes AGSR and the FAB-G supervised multi-agent framework that predicts attribute salience from human annotations to constrain MLLM emotion reasoning, yielding gains on EmoArt and cross-dataset tests.
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C2F-Thinker: Coarse-to-Fine Reasoning with Hint-Guided Reinforcement Learning for Multimodal Sentiment Analysis
C2F-Thinker combines structured coarse-to-fine chain-of-thought reasoning with hint-guided GRPO reinforcement learning to achieve competitive fine-grained sentiment regression and superior cross-domain generalization in multimodal analysis.
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MER 2026: From Discriminative Emotion Recognition to Generative Emotion Understanding
MER2026 defines four tracks to advance generative emotion understanding from individual basic labels to dyadic, fine-grained, preference, and physiological scenarios.