R²ScP recovers missing audio-visual data in question answering by retrieving semantically consistent examples and purifying noise, outperforming generative imputation in incomplete scenarios.
InProceed- ings of the AAAI Conference on Artificial Intelligence, volume 39, pages 10483–10491
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AffectAgent deploys a query planner, evidence filter, and emotion generator as collaborative agents trained via MAPPO with shared reward, plus MB-MoE and RAAF modules, to achieve superior multimodal emotion recognition on MER-UniBench.
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Retrieving to Recover: Towards Incomplete Audio-Visual Question Answering via Semantic-consistent Purification
R²ScP recovers missing audio-visual data in question answering by retrieving semantically consistent examples and purifying noise, outperforming generative imputation in incomplete scenarios.
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AffectAgent: Collaborative Multi-Agent Reasoning for Retrieval-Augmented Multimodal Emotion Recognition
AffectAgent deploys a query planner, evidence filter, and emotion generator as collaborative agents trained via MAPPO with shared reward, plus MB-MoE and RAAF modules, to achieve superior multimodal emotion recognition on MER-UniBench.