activity
20232026
collaborators

7 papers

cs.CV2026

DIME: Scaling Facial Representation Learning via Differential Masked Autoencoding

Hao Yu, Haoyu Chen, Hui Wei +3

Scaling up data and model capacity has driven substantial progress in visual representation learning. However, conventional pretext tasks exhibit diminishing returns on fine-graine…

cs.CV2026

Human-JEPA: A Human-Centric Vision Model that Perceives and Anticipates

Hui Wei, Licai Sun, Guoying Zhao

Machines that understand humans should perceive the present and anticipate the future. Existing human-centric vision model are pretrained on human images, set the state of the art…

cs.CV2026

Face De-Identification: A Domain-Centric Survey from Capture to Processing

Hui Wei, Hao Yu, Guoying Zhao

Face de-identification (De-ID) aims to remove or conceal personally identifiable facial features in images or videos to prevent identity recognition while preserving utility for do…

cs.CV2026

CanonicalPhys: Pose-Robust Remote Photoplethysmography via Canonical-Space Priors

Hui Wei, Seyedata Jodeiri Seyedian, Xiaobai Li +1

Deep remote photoplethysmography (rPPG) attains sub-bpm heart-rate error on frontal, stationary faces yet degrades sharply under head pose: on MMPD, the state-of-the-art FactorizeP…

cs.CV2026

iMiGUE-3K: A Large-Scale Benchmark for Micro-Gesture Analysis with Self-Supervised Learning

Chengyan Wang, Haoyu Chen, Hui Wei +3

Emotion understanding is a fundamental challenge in affective computing and artificial intelligence. While existing approaches predominantly focus on facial expressions and speech,…

cs.LG2024

Temporally Multi-Scale Sparse Self-Attention for Physical Activity Data Imputation

Hui Wei, Maxwell A. Xu, Colin Samplawski +3

Wearable sensors enable health researchers to continuously collect data pertaining to the physiological state of individuals in real-world settings. However, such data can be subje…

cs.LG2023

REBAR: Retrieval-Based Reconstruction for Time-series Contrastive Learning

Maxwell A. Xu, Alexander Moreno, Hui Wei +2

The success of self-supervised contrastive learning hinges on identifying positive data pairs, such that when they are pushed together in embedding space, the space encodes useful…