Papers Multimodal Sentiment Analysis
“Multimodal Sentiment Analysis” 태그가 달린 논문 249편 · 필터 해제
Contrastive Mixed Prompt Learning for Incomplete Multimodal Sentiment Analysis with Unseen Modality Combination
Incomplete multimodal sentiment analysis has garnered significant attention in recent years. Existing approaches typically assume that data is missing at random or are designed specifically for certain missing patterns, …
Multimodal Sentiment AnalysisContrastive LearningRobust Incomplete Multimodal Sentiment Analysis via Iterative Proxy Correction
Multimodal sentiment analysis aims to infer affective states by integrating language, visual, and acoustic cues. However, real-world multimodal inputs are often incomplete or corrupted, which can weaken cross-modal compl…
Multimodal Sentiment AnalysisMultimodal ReasoningMulti-Granularity Sentiment Integration for LLM-Based Multimodal Sentiment Analysis
Multimodal sentiment analysis (MSA) aims to predict sentiment polarity and intensity from heterogeneous inputs such as text, audio, and vision. While large language models (LLMs) offer strong semantic priors for MSA, eff…
Multimodal Sentiment AnalysisRethinking Modality Reliability in Multimodal Sentiment Analysis with Incomplete Observations
Multimodal Sentiment Analysis (MSA) integrates text, audio, and vision to infer human affect, yet real-world multimodal observations are often incomplete. Existing methods for incomplete-observation MSA mainly follow two…
Multimodal Sentiment AnalysisRepresentation LearningSemantic-Aligned Structural Abstraction for Multimodal Sentiment Analysis
Multimodal Sentiment Analysis (MSA) aims to interpret complex human emotions by integrating natural language with non-verbal modalities. Non-verbal modalities share a structural isomorphism with natural language, as both…
Multimodal Sentiment AnalysisMRUF: Multi-granularity Routing with Uncertainty-Aware Fusion for Robust Multimodal Sentiment Analysis
Multimodal sentiment analysis relies on language, visual, and acoustic cues, but utterance-level modality quality may vary due to occlusion, background noise, motion blur, or imperfect transcripts, causing conventional f…
Multimodal Sentiment AnalysisExplicit Representation Alignment for Multimodal Sentiment Analysis
Multimodal affective analysis aims to understand human sentiment and emotion by jointly modeling heterogeneous modalities such as text and images. However, multimodal models often fail to consistently outperform strong t…
Multimodal Sentiment AnalysisTRACE: A Temporal Conditional Estimation for Multimodal Time Series Foundation Models
Time series foundation models (TS-FMs) aim to learn generalizable temporal representations that can be adapted to a wide range of downstream tasks. In real-world multimodal settings, time series are frequently affected b…
Multimodal Sentiment AnalysisA Conflict-Aware Penalty and Statistical Loss Framework for Balancing Modalities and Enhancing Stability in Multimodal Sentiment Analysis
Multimodal Sentiment Analysis (MSA) fuses text, acoustic, and visual streams to infer sentiment. Because pre-trained text encoders are far more expressive than their acoustic and visual counterparts, the text modality te…
Multimodal Sentiment AnalysisControlling Decision Drift in Multimodal Sentiment Analysis with Missing Modalities
Multimodal sentiment analysis relies on textual, acoustic, and visual signals, yet real-world data often suffer from modality missing and quality imbalance. Existing methods generate features for modality missing from av…
Multimodal Sentiment AnalysisEnhance-then-Balance Modality Collaboration for Robust Multimodal Sentiment Analysis
Multimodal sentiment analysis (MSA) integrates heterogeneous text, audio, and visual signals to infer human emotions. While recent approaches leverage cross-modal complementarity, they often struggle to fully utilize wea…
Multimodal Sentiment AnalysisEvaluation Before Generation: A Paradigm for Robust Multimodal Sentiment Analysis with Missing Modalities
The missing modality problem poses a fundamental challenge in multimodal sentiment analysis, significantly degrading model accuracy and generalization in real world scenarios. Existing approaches primarily improve robust…
Multimodal Sentiment AnalysisQA-MoE: Towards a Continuous Reliability Spectrum with Quality-Aware Mixture of Experts for Robust Multimodal Sentiment Analysis
Multimodal Sentiment Analysis (MSA) aims to infer human sentiment from textual, acoustic, and visual signals. In real-world scenarios, however, multimodal inputs are often compromised by dynamic noise or modality missing…
Multimodal Sentiment AnalysisCAGMamba: Context-Aware Gated Cross-Modal Mamba Network for Multimodal Sentiment Analysis
Multimodal Sentiment Analysis (MSA) requires effective modeling of cross-modal interactions and contextual dependencies while remaining computationally efficient. Existing fusion approaches predominantly rely on Transfor…
Multimodal Sentiment AnalysisProgressive Representation Learning for Multimodal Sentiment Analysis with Incomplete Modalities
Multimodal Sentiment Analysis (MSA) seeks to infer human emotions by integrating textual, acoustic, and visual cues. However, existing approaches often rely on all modalities are completeness, whereas real-world applicat…
Multimodal Sentiment AnalysisRepresentation LearningC2F-Thinker: Coarse-to-Fine Reasoning with Hint-Guided Reinforcement Learning for Multimodal Sentiment Analysis
Multimodal sentiment analysis aims to integrate textual, acoustic, and visual information for deep emotional understanding. Despite the progress of multimodal large language models (MLLMs) via supervised fine-tuning, the…
Multimodal Sentiment AnalysisReinforcement LearningDomain GeneralizationFedUAF: Uncertainty-Aware Fusion with Reliability-Guided Aggregation for Multimodal Federated Sentiment Analysis
Multimodal sentiment analysis in federated learning environments faces significant challenges due to missing modalities, heterogeneous data distributions, and unreliable client updates. Existing federated approaches ofte…
Multimodal Sentiment AnalysisFederated LearningTri-Subspaces Disentanglement for Multimodal Sentiment Analysis
Multimodal Sentiment Analysis (MSA) integrates language, visual, and acoustic modalities to infer human sentiment. Most existing methods either focus on globally shared representations or modality-specific features, whil…
Multimodal Intent RecognitionMultimodal Sentiment AnalysisMissing-by-Design: Certifiable Modality Deletion for Revocable Multimodal Sentiment Analysis
As multimodal systems increasingly process sensitive personal data, the ability to selectively revoke specific data modalities has become a critical requirement for privacy compliance and user autonomy. We present Missin…
Multimodal Sentiment AnalysisRepresentation LearningLeveraging Textual-Cues for Enhancing Multimodal Sentiment Analysis by Object Recognition
Multimodal sentiment analysis, which includes both image and text data, presents several challenges due to the dissimilarities in the modalities of text and image, the ambiguity of sentiment, and the complexities of cont…
Multimodal Sentiment AnalysisObject Recognition