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.
OV-MER: Towards open-vocabulary multimodal emotion recognition
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MODF-SIR is a multi-agent omni-modal distilled framework achieving state-of-the-art social intelligence reasoning results using 30% of training data via distillation, TTA, and LoRA.
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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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MODF-SIR: A Multi-agent Omni-modal Distilled Framework for Social Intelligence Reasoning
MODF-SIR is a multi-agent omni-modal distilled framework achieving state-of-the-art social intelligence reasoning results using 30% of training data via distillation, TTA, and LoRA.