Multimodal Emotion Recognition
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Benchmarks
IEMOCAP-4
MELD
IEMOCAP
CMU-MOSEI-Sentiment
CMU-MOSEI-Sentiment-3
MELD-Sentiment
Most implemented
Multimodal Speech Emotion Recognition and Ambiguity Resolution
Multimodal Speech Emotion Recognition Using Audio and Text
MER 2023: Multi-label Learning, Modality Robustness, and Semi-Supervised Learning
Complementary Fusion of Multi-Features and Multi-Modalities in Sentiment Analysis
Papers
Affect-Prototype Guided Fusion for Open-Vocabulary Incomplete Multi-modal Emotion Recognition
Open-vocabulary multimodal emotion recognition (OV-MER) aims to generate open natural-language emotion labels from multimodal affective cues. In real-world scenarios, however, complete and synchronized modal data are dif…
Multimodal Emotion RecognitionMultimodal Emotion Recognition in Conversations via Class-Wise Adaptive Modality Fusion and Affective Geometry
Emotion Recognition in Conversations (ERC) requires integrating heterogeneous textual, audio, and visual cues while accounting for conversational context and emotional dynamics. We extend the Self-Distillation Transforme…
Multimodal Emotion RecognitionEnhancing Multimodal Emotion Recognition via Multi-Feature Encoding and Attention-Based Fusion
Multimodal emotion recognition has attracted growing interest due to its importance in human-computer interaction, remote education, and healthcare. This paper proposes a novel multimodal emotion recognition framework th…
Multimodal Emotion RecognitionModality Disentangled Learning for Incomplete Multimodal Emotion Recognition: A Primitive Memory Distillation Perspective
Multimodal Emotion Recognition (MER) systems often suffer from missing modalities in real-world scenarios. Existing methods usually generate, align, or distill missing modalities as a whole, overlooking the heterogeneous…
Multimodal Emotion RecognitionTTSD-FAR: Test-Time Self-Distillation with Fisher-Anchored Restoration for Missing-Modality Emotion Recognition in LVLMs
Large video-language models (LVLMs) have shown remarkable performance on multimodal tasks like multimodal emotion recognition (ER) in the wild. ER is inherently multimodal, requiring a joint understanding of facial expre…
Multimodal Emotion RecognitionRationale-Guided Learning for Multimodal Emotion Recognition
Multimodal emotion recognition in conversation (MERC) requires understanding complex interactions between verbal and non-verbal cues. However, most existing approaches fundamentally treat this as a direct input-output (m…
Multimodal Emotion Recognition