most citedDecoupled Hierarchical Distillation for Multimodal Emotion Recognition

2 citations · 2 across the 4 of their papers we have counts for

collaborators

11 papers

cs.CV2026

Hierarchical Vision-Language Interaction for Facial Action Unit Detection

Yong Li, Yi Ren, Yizhe Zhang +5

Facial Action Unit (AU) detection seeks to recognize subtle facial muscle activations as defined by the Facial Action Coding System (FACS). A primary challenge w.r.t AU detection i…

cs.CV20262 cited

Decoupled Hierarchical Distillation for Multimodal Emotion Recognition

Yong Li, Yuanzhi Wang, Yi Ding +3

Human multimodal emotion recognition (MER) seeks to infer human emotions by integrating information from language, visual, and acoustic modalities. Although existing MER approaches…

cs.LG2026

EEG-DLite: Dataset Distillation for Efficient Large EEG Model Training

Yuting Tang, Weibang Jiang, Shanglin Li +5

Large-scale EEG foundation models have shown strong generalization across a range of downstream tasks, but their training remains resource-intensive due to the volume and variable…

cs.CL2026

DepFlow: Disentangled Speech Generation to Mitigate Semantic Bias in Depression Detection

Yuxin Li, Xiangyu Zhang, Yifei Li +4

Speech is a scalable and non-invasive biomarker for early mental health screening. However, widely used depression datasets like DAIC-WOZ exhibit strong coupling between linguistic…

cs.LG2025

LEAF: Language-EEG Aligned Foundation Model for Brain-Computer Interfaces

Muyun Jiang, Shuailei Zhang, Zhenjie Yang +9

Recent advances in electroencephalography (EEG) foundation models, which capture transferable EEG representations, have greatly accelerated the development of brain-computer interf…

eess.SP2025

EEG-to-Gait Decoding via Phase-Aware Representation Learning

Xi Fu, Weibang Jiang, Rui Liu +2

Accurate decoding of lower-limb motion from EEG signals is essential for advancing brain-computer interface (BCI) applications in movement intent recognition and control. This stud…