Tag
The paper proposes Light-MER, a lightweight multimodal emotion recognition framework that uses knowledge distillation from an 8B teacher model to a sub-1B student, achieving state-of-the-art performance with significantly higher inference efficiency, challenging the necessity of models larger than 1B parameters.
This paper introduces MER-R1, a reinforcement learning framework that synergizes fast and slow thinking for multimodal emotion recognition. It achieves state-of-the-art performance by jointly optimizing recall and precision through dual-objective disentanglement and slow-fast confidence calibration.