Introduces the eJSL Dialog dataset (1,920 videos in 480 dialogues from STUDIES corpus) for conversational sign language emotion recognition and benchmarks models revealing a domain gap with generic multimodal approaches.
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A subject-invariant cross-modal prompt-tuning method with decoupled shared-specific adapters fuses facial and rPPG features in a frozen ViT to improve video-based emotion recognition accuracy and cross-subject generalization on MAHNOB-HCI and DEAP.
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Emotion Recognition in Sign Language Conversation
Introduces the eJSL Dialog dataset (1,920 videos in 480 dialogues from STUDIES corpus) for conversational sign language emotion recognition and benchmarks models revealing a domain gap with generic multimodal approaches.
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Adaptive Physical-Facial Representation Fusion via Subject-Invariant Cross-Modal Prompt Tuning for Video-Based Emotion Recognition
A subject-invariant cross-modal prompt-tuning method with decoupled shared-specific adapters fuses facial and rPPG features in a frozen ViT to improve video-based emotion recognition accuracy and cross-subject generalization on MAHNOB-HCI and DEAP.