MOTOR-Bench supplies a real-world video dataset for structured mental state understanding in learning settings, while MOTOR-MAS improves zero-shot prediction of behavior, cognition, and emotion labels over single models and other multi-agent systems.
Affectgpt: A new dataset, model, and benchmark for emotion understanding with multimodal large language models
3 Pith papers cite this work. Polarity classification is still indexing.
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EmoTrans is a new video benchmark with four progressive tasks that measures how well current multimodal LLMs handle dynamic emotion transitions rather than static recognition.
EmoS is a new high-fidelity benchmark for fine-grained streaming emotional understanding that produces measurable gains when used to fine-tune multimodal large language models.
citing papers explorer
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MOTOR-Bench: A Real-world Dataset and Multi-agent Framework for Zero-shot Human Mental State Understanding
MOTOR-Bench supplies a real-world video dataset for structured mental state understanding in learning settings, while MOTOR-MAS improves zero-shot prediction of behavior, cognition, and emotion labels over single models and other multi-agent systems.
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EmoTrans: A Benchmark for Understanding, Reasoning, and Predicting Emotion Transitions in Multimodal LLMs
EmoTrans is a new video benchmark with four progressive tasks that measures how well current multimodal LLMs handle dynamic emotion transitions rather than static recognition.