MuDD dataset plus GSR-guided progressive distillation with dynamic routing achieves state-of-the-art non-contact deception detection and concealed-digit identification.
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A latent denoising objective with saliency-aware corruption and contrastive distillation improves visual alignment and corruption robustness in large multimodal models.
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MuDD: A Multimodal Deception Detection Dataset and GSR-Guided Progressive Distillation for Non-Contact Deception Detection
MuDD dataset plus GSR-guided progressive distillation with dynamic routing achieves state-of-the-art non-contact deception detection and concealed-digit identification.
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Latent Denoising Improves Visual Alignment in Large Multimodal Models
A latent denoising objective with saliency-aware corruption and contrastive distillation improves visual alignment and corruption robustness in large multimodal models.