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ConsMSA introduces semantic distribution consistency learning across intra- and inter-modalities for multimodal sentiment analysis, enabling token reweighting and pruning while achieving state-of-the-art results on CMU-MOSI, CMU-MOSEI, and CH-SIMS with only 10% of tokens retained.
The paper introduces a Reliability-aware Cross-sample Enhancement (RCE) framework for robust multimodal sentiment analysis, addressing noise and missing modalities through adaptive information bottleneck and cross-sample enhancement, demonstrating superior performance across various settings.
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.
This paper proposes an iterative proxy correction framework to enhance robustness in multimodal sentiment analysis when dealing with incomplete or corrupted inputs by refining a language proxy for better sentiment prediction.
The paper addresses Incomplete Multimodal Sentiment Analysis with Unseen Modality Combinations and proposes the ContrastiveMixedPromptLearning (CMPL) model, which uses label-guided contrastive learning and modality-combination prompts to enhance generalization, achieving over 5% accuracy improvement on benchmark datasets.
The paper proposes SentiLLM, a framework that uses semantic-aligned structural abstraction to distill non-verbal modalities into text-like tokens for multimodal sentiment analysis with LLMs. It introduces a dual-stream salience-context calibration mechanism and achieves superior performance on four datasets.