486 citations · 2.3k across the 55 of their papers we have counts for
8 papers · 1 filter
Seeing Through the Noisy Dark: Towards Real-world Low-Light Image Enhancement and Denoising
Jiahuan Ren, Zhao Zhang, Richang Hong +3
Low-light image enhancement (LLIE) aims at improving the illumination and visibility of dark images with lighting noise. To handle the real-world low-light images often with heavy…
Decoupled Cross-Scale Cross-View Interaction for Stereo Image Enhancement in The Dark
Huan Zheng, Zhao Zhang, Jicong Fan +3
Low-light stereo image enhancement (LLSIE) is a relatively new task to enhance the quality of visually unpleasant stereo images captured in dark condition. However, current methods…
Switchable Online Knowledge Distillation
Biao Qian, Yang Wang, Hongzhi Yin +2
Online Knowledge Distillation (OKD) improves the involved models by reciprocally exploiting the difference between teacher and student. Several crucial bottlenecks over the gap bet…
Emotion Separation and Recognition from a Facial Expression by Generating the Poker Face with Vision Transformers
Jia Li, Jiantao Nie, Dan Guo +2
Representation learning and feature disentanglement have garnered significant research interest in the field of facial expression recognition (FER). The inherent ambiguity of emoti…
A Review-aware Graph Contrastive Learning Framework for Recommendation
Jie Shuai, Kun Zhang, Le Wu +4
Most modern recommender systems predict users preferences with two components: user and item embedding learning, followed by the user-item interaction modeling. By utilizing the au…
Towards Feature Distribution Alignment and Diversity Enhancement for Data-Free Quantization
Yangcheng Gao, Zhao Zhang, Richang Hong +3
To obtain lower inference latency and less memory footprint of deep neural networks, model quantization has been widely employed in deep model deployment, by converting the floatin…
FCL-GAN: A Lightweight and Real-Time Baseline for Unsupervised Blind Image Deblurring
Suiyi Zhao, Zhao Zhang, Richang Hong +3
Blind image deblurring (BID) remains a challenging and significant task. Benefiting from the strong fitting ability of deep learning, paired data-driven supervised BID method has o…
Automatic Depression Detection via Learning and Fusing Features from Visual Cues
Yanrong Guo, Chenyang Zhu, Shijie Hao +1
Depression is one of the most prevalent mental disorders, which seriously affects one's life. Traditional depression diagnostics commonly depends on rating with scales, which can b…