486 citations · 2.3k across the 55 of their papers we have counts for
55 papers
Clarity ChatGPT: An Interactive and Adaptive Processing System for Image Restoration and Enhancement
Yanyan Wei, Zhao Zhang, Jiahuan Ren +5
The generalization capability of existing image restoration and enhancement (IRE) methods is constrained by the limited pre-trained datasets, making it difficult to handle agnostic…
Generative Contrastive Graph Learning for Recommendation
Yonghui Yang, Zhengwei Wu, Le Wu +5
By treating users' interactions as a user-item graph, graph learning models have been widely deployed in Collaborative Filtering(CF) based recommendation. Recently, researchers hav…
Unlearnable Examples Give a False Sense of Security: Piercing through Unexploitable Data with Learnable Examples
Wan Jiang, Yunfeng Diao, He Wang +3
Safeguarding data from unauthorized exploitation is vital for privacy and security, especially in recent rampant research in security breach such as adversarial/membership attacks.…
Multimodal Feature Extraction and Fusion for Emotional Reaction Intensity Estimation and Expression Classification in Videos with Transformers
Jia Li, Yin Chen, Xuesong Zhang +6
In this paper, we present our advanced solutions to the two sub-challenges of Affective Behavior Analysis in the wild (ABAW) 2023: the Emotional Reaction Intensity (ERI) Estimation…
Adaptive Data-Free Quantization
Biao Qian, Yang Wang, Richang Hong +1
Data-free quantization (DFQ) recovers the performance of quantized network (Q) without the original data, but generates the fake sample via a generator (G) by learning from full-pr…
Improving Recommendation Fairness via Data Augmentation
Lei Chen, Le Wu, Kun Zhang +5
Collaborative filtering based recommendation learns users' preferences from all users' historical behavior data, and has been popular to facilitate decision making. R Recently, the…
Rethinking Data-Free Quantization as a Zero-Sum Game
Biao Qian, Yang Wang, Richang Hong +1
Data-free quantization (DFQ) recovers the performance of quantized network (Q) without accessing the real data, but generates the fake sample via a generator (G) by learning from f…
Contrastive Video Question Answering via Video Graph Transformer
Junbin Xiao, Pan Zhou, Angela Yao +4
We propose to perform video question answering (VideoQA) in a Contrastive manner via a Video Graph Transformer model (CoVGT). CoVGT's uniqueness and superiority are three-fold: 1)…
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…
Few-shot Learning with Global Relatedness Decoupled-Distillation
Yuan Zhou, Yanrong Guo, Shijie Hao +3
Despite the success that metric learning based approaches have achieved in few-shot learning, recent works reveal the ineffectiveness of their episodic training mode. In this paper…
Privileged Graph Distillation for Cold Start Recommendation
Shuai Wang, Kun Zhang, Le Wu +3
The cold start problem in recommender systems is a long-standing challenge, which requires recommending to new users (items) based on attributes without any historical interaction…
Set2setRank: Collaborative Set to Set Ranking for Implicit Feedback based Recommendation
Lei Chen, Le Wu, Kun Zhang +2
As users often express their preferences with binary behavior data~(implicit feedback), such as clicking items or buying products, implicit feedback based Collaborative Filtering~(…
Few-shot Partial Multi-view Learning
Yuan Zhou, Yanrong Guo, Shijie Hao +2
It is often the case that data are with multiple views in real-world applications. Fully exploring the information of each view is significant for making data more representative.…
Fine-Grained Fashion Similarity Prediction by Attribute-Specific Embedding Learning
Jianfeng Dong, Zhe Ma, Xiaofeng Mao +4
This paper strives to predict fine-grained fashion similarity. In this similarity paradigm, one should pay more attention to the similarity in terms of a specific design/attribute…
Revisiting Local Descriptor for Improved Few-Shot Classification
Jun He, Richang Hong, Xueliang Liu +2
Few-shot classification studies the problem of quickly adapting a deep learner to understanding novel classes based on few support images. In this context, recent research efforts…
Learning Fair Representations for Recommendation: A Graph-based Perspective
Le Wu, Lei Chen, Pengyang Shao +3
As a key application of artificial intelligence, recommender systems are among the most pervasive computer aided systems to help users find potential items of interests. Recently,…
Deep Adversarial Inconsistent Cognitive Sampling for Multi-view Progressive Subspace Clustering
Renhao Sun
Deep multi-view clustering methods have achieved remarkable performance. However, all of them failed to consider the difficulty labels (uncertainty of ground-truth for training sam…