gair-lab-3c07837c·1 events·first seen Aliases: GAIR Lab
Researchers from GAIR Lab propose Light-MER, a lightweight framework for multimodal emotion recognition (MER) that uses knowledge distillation to transfer capabilities from large teacher models (7B+) to sub-1B student models. Two novel optimization strategies are introduced: an optimal transport loss combining Sliced Wasserstein Distance with hidden-state alignment, and a multi-reward optimization strategy based on GRPO. Experiments across nine benchmark datasets show Light-MER achieves state-of-the-art performance with substantially improved inference efficiency, challenging the assumption that large models are necessary for high-quality MER.