Author

Feng-Qi Cui

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Book Open access Nov 2026

SAIN: A Hierarchical Dynamics-Aware EEG–fNIRS Framework for Continuous Affect Estimation

Continuous affect estimation from electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) aims to recover time-varying Valence and Arousal from heterogeneous physiological signals. Their differences in channel organisation, response latency, temporal statistics, and noise characteristics make hom...

Feng-Qi Cui, Jin-Yang Huang, Jia-Cheng Zhao et al. · 0 citations

Preprint Aug 2026

DynaForcing: Overcoming Dynamic Collapse in Self-Forcing Distillation for Streaming Avatar Generation

Audio-driven avatar generation requires realistic lip-sync, expressive motion, and real-time streaming. Recent work achieves the latter via self-forcing with Distribution Matching Distillation (DMD), but this paradigm suffers from a critical failure that has not been systematically characterized: dynamic collapse, wher...

Yubo Huang, Si-Rui Zhao, Xinchen Yao et al. · 1 citation · ⚡1

#artificial intelligence Preprint Apr 2026

EviDep: Uncertainty-Aware Multimodal Depression Estimation via Disentangled Evidential Learning

EviDep is presented, a multimodal evidential regression framework that integrates multi-scale temporal modeling and shared--private representation learning for uncertainty-aware depression estimation and shows competitive prediction accuracy, while both uncertainty estimates generally increase under controlled feature...

Fang-Yuan Liu, Si-Rui Zhao, Yang-Song Zhang et al. · 0 citations

2026

Toward Trustworthy Dynamic Facial Expression Recognition via Information Bottleneck Modeling

Due to the presence of semantic ambiguity among similar expression categories and the inherent imbalance in spatio-temporal feature intensities, dynamic facial expression recognition (DFER) in the wild poses significant challenges for building trustworthy and robust systems. These factors often lead to inconsistent fea...

Feng-Qi Cui, Anyang Tong, Jin-Yang Huang et al. · IEEE Transactions on Informa... · 1 citation

Preprint Aug 2026

Diversity Matters: Distributional Feature Coverage Sample Selection for Data-Efficient Backdoor Attacks

DFCS is proposed, a training-free, trigger-agnostic method that clusters fixed pretrained features into one region per poisoning slot and selects the centroid-nearest sample from each region and supports distributional feature coverage as an effective selection principle for low-budget dirty-label backdoor attacks.

Yi Yang, Xiaoke Chen, Jin-Yang Huang et al. · 0 citations