MISID is a multimodal multi-turn dataset for intent recognition in strategic deception games, paired with the FRACTAM framework that improves MLLM performance on hidden intent detection via decouple-anchor-reason steps.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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The Dual-Branch Rebalancing Framework (DBR) mitigates shared-private branch imbalance in multimodal sentiment analysis via Temporal-Structural Factorization, Anchor-Guided Private Routing, and Bidirectional Rebalancing Fusion, outperforming baselines on CMU-MOSI, CMU-MOSEI, and MIntRec.
citing papers explorer
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MISID: A Multimodal Multi-turn Dataset for Complex Intent Recognition in Strategic Deception Games
MISID is a multimodal multi-turn dataset for intent recognition in strategic deception games, paired with the FRACTAM framework that improves MLLM performance on hidden intent detection via decouple-anchor-reason steps.
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Mitigating Shared-Private Branch Imbalance via Dual-Branch Rebalancing for Multimodal Sentiment Analysis
The Dual-Branch Rebalancing Framework (DBR) mitigates shared-private branch imbalance in multimodal sentiment analysis via Temporal-Structural Factorization, Anchor-Guided Private Routing, and Bidirectional Rebalancing Fusion, outperforming baselines on CMU-MOSI, CMU-MOSEI, and MIntRec.