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This paper proposes an Affect-Prototype-Conditioned Fusion (APCF) framework for open-vocabulary multimodal emotion recognition with incomplete modalities, using an affect-prototype library to guide feature fusion and an LLM decoder for generating natural language emotion labels.
This paper introduces a semantic-aware completeness estimation method for robust multimodal sentiment analysis with incomplete modalities, aiming to improve accuracy when sentiment-related cues are missing.
Presents GAUGE, a lightweight counterfactual gating framework that handles incomplete multimodal inputs by scoring fine-grained evidence units with Taylor approximation and applying continuous gates for reliable prediction.