AffectGPT-RL applies reinforcement learning to optimize non-differentiable emotion wheel metrics in open-vocabulary multimodal emotion recognition, yielding performance gains and state-of-the-art results on basic emotion recognition benchmarks.
Mer 2024: Semi-supervised learning, noise robustness, and open-vocabulary multimodal emotion recognition
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MER2026 defines four tracks to advance generative emotion understanding from individual basic labels to dyadic, fine-grained, preference, and physiological scenarios.
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AffectGPT-RL: Revealing Roles of Reinforcement Learning in Open-Vocabulary Emotion Recognition
AffectGPT-RL applies reinforcement learning to optimize non-differentiable emotion wheel metrics in open-vocabulary multimodal emotion recognition, yielding performance gains and state-of-the-art results on basic emotion recognition benchmarks.
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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.