HapticLDM is the first latent diffusion model that generates vibrotactile signals directly from text, using dynamic text curation and global denoising to improve realism and semantic alignment over autoregressive baselines.
A circumplex model of affect
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Proposes AGSR and the FAB-G supervised multi-agent framework that predicts attribute salience from human annotations to constrain MLLM emotion reasoning, yielding gains on EmoArt and cross-dataset tests.
Proposes TA2CL framework that uses temporal asynchronous alignment in contrastive learning to improve cross-subject EEG emotion classification, reporting 64.5% accuracy on 9-class FACED, 79.5% binary on FACED, 86.4% on SEED and 70.1% on SEED-V.
citing papers explorer
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HapticLDM: A Diffusion Model for Text-to-Vibrotactile Generation
HapticLDM is the first latent diffusion model that generates vibrotactile signals directly from text, using dynamic text curation and global denoising to improve realism and semantic alignment over autoregressive baselines.
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Attribute-Grounded Selective Reasoning for Artwork Emotion Understanding with Multimodal Large Language Models
Proposes AGSR and the FAB-G supervised multi-agent framework that predicts attribute salience from human annotations to constrain MLLM emotion reasoning, yielding gains on EmoArt and cross-dataset tests.
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Cross-Subject EEG Emotion Recognition Based on Temporal Asynchronous Alignment Contrastive Learning
Proposes TA2CL framework that uses temporal asynchronous alignment in contrastive learning to improve cross-subject EEG emotion classification, reporting 64.5% accuracy on 9-class FACED, 79.5% binary on FACED, 86.4% on SEED and 70.1% on SEED-V.