281 citations · 665 across the 22 of their papers we have counts for
22 papers
Domain Adaptive Semantic Segmentation without Source Data
Fuming You, Jingjing Li, Lei Zhu +3
Domain adaptive semantic segmentation is recognized as a promising technique to alleviate the domain shift between the labeled source domain and the unlabeled target domain in many…
Two-pronged Strategy: Lightweight Augmented Graph Network Hashing for Scalable Image Retrieval
Hui Cui, Lei Zhu, Jingjing Li +2
Hashing learns compact binary codes to store and retrieve massive data efficiently. Particularly, unsupervised deep hashing is supported by powerful deep neural networks and has th…
Adversarial Energy Disaggregation for Non-intrusive Load Monitoring
Zhekai Du, Jingjing Li, Lei Zhu +2
Energy disaggregation, also known as non-intrusive load monitoring (NILM), challenges the problem of separating the whole-home electricity usage into appliance-specific individual…
Cross-Domain Gradient Discrepancy Minimization for Unsupervised Domain Adaptation
Zhekai Du, Jingjing Li, Hongzu Su +2
Unsupervised Domain Adaptation (UDA) aims to generalize the knowledge learned from a well-labeled source domain to an unlabeled target domain. Recently, adversarial domain adaptati…
UGRec: Modeling Directed and Undirected Relations for Recommendation
Xinxiao Zhao, Zhiyong Cheng, Lei Zhu +2
Recommender systems, which merely leverage user-item interactions for user preference prediction (such as the collaborative filtering-based ones), often face dramatic performance d…
Interest-aware Message-Passing GCN for Recommendation
Fan Liu, Zhiyong Cheng, Lei Zhu +2
Graph Convolution Networks (GCNs) manifest great potential in recommendation. This is attributed to their capability on learning good user and item embeddings by exploiting the col…
Feature-level Attentive ICF for Recommendation
Zhiyong Cheng, Fan Liu, Shenghan Mei +3
Item-based collaborative filtering (ICF) enjoys the advantages of high recommendation accuracy and ease in online penalization and thus is favored by the industrial recommender sys…
Dual-level Semantic Transfer Deep Hashing for Efficient Social Image Retrieval
Lei Zhu, Hui Cui, Zhiyong Cheng +2
Social network stores and disseminates a tremendous amount of user shared images. Deep hashing is an efficient indexing technique to support large-scale social image retrieval, due…
Task-adaptive Asymmetric Deep Cross-modal Hashing
Fengling Li, Tong Wang, Lei Zhu +2
Supervised cross-modal hashing aims to embed the semantic correlations of heterogeneous modality data into the binary hash codes with discriminative semantic labels. Because of its…
Multi-Feature Discrete Collaborative Filtering for Fast Cold-start Recommendation
Yang Xu, Lei Zhu, Zhiyong Cheng +2
Hashing is an effective technique to address the large-scale recommendation problem, due to its high computation and storage efficiency on calculating the user preferences on items…
A^2-GCN: An Attribute-aware Attentive GCN Model for Recommendation
Fan Liu, Zhiyong Cheng, Lei Zhu +2
As important side information, attributes have been widely exploited in the existing recommender system for better performance. In the real-world scenarios, it is common that some…
Cycle-consistent Conditional Adversarial Transfer Networks
Jingjing Li, Erpeng Chen, Zhengming Ding +3
Domain adaptation investigates the problem of cross-domain knowledge transfer where the labeled source domain and unlabeled target domain have distinctive data distributions. Recen…
Alleviating Feature Confusion for Generative Zero-shot Learning
Jingjing Li, Mengmeng Jing, Ke Lu +3
Lately, generative adversarial networks (GANs) have been successfully applied to zero-shot learning (ZSL) and achieved state-of-the-art performance. By synthesizing virtual unseen…
Personalized Hashtag Recommendation for Micro-videos
Yinwei Wei, Zhiyong Cheng, Xuzheng Yu +3
Personalized hashtag recommendation methods aim to suggest users hashtags to annotate, categorize, and describe their posts. The hashtags, that a user provides to a post (e.g., a m…
From Zero-Shot Learning to Cold-Start Recommendation
Jingjing Li, Mengmeng Jing, Ke Lu +3
Zero-shot learning (ZSL) and cold-start recommendation (CSR) are two challenging problems in computer vision and recommender system, respectively. In general, they are independentl…
Discrete Optimal Graph Clustering
Yudong Han, Lei Zhu, Zhiyong Cheng +2
Graph based clustering is one of the major clustering methods. Most of it work in three separate steps: similarity graph construction, clustering label relaxing and label discretiz…
Adaptive Collaborative Similarity Learning for Unsupervised Multi-view Feature Selection
Xiao Dong, Lei Zhu, Xuemeng Song +2
In this paper, we investigate the research problem of unsupervised multi-view feature selection. Conventional solutions first simply combine multiple pre-constructed view-specific…
Exploring Auxiliary Context: Discrete Semantic Transfer Hashing for Scalable Image Retrieval
Lei Zhu, Zi Huang, Zhihui Li +2
Unsupervised hashing can desirably support scalable content-based image retrieval (SCBIR) for its appealing advantages of semantic label independence, memory and search efficiency.…
Leveraging the Invariant Side of Generative Zero-Shot Learning
Jingjing Li, Mengmeng Jin, Ke Lu +3
Conventional zero-shot learning (ZSL) methods generally learn an embedding, e.g., visual-semantic mapping, to handle the unseen visual samples via an indirect manner. In this paper…
Fusion-supervised Deep Cross-modal Hashing
Li Wang, Lei Zhu, En Yu +2
Deep hashing has recently received attention in cross-modal retrieval for its impressive advantages. However, existing hashing methods for cross-modal retrieval cannot fully captur…
MMALFM: Explainable Recommendation by Leveraging Reviews and Images
Zhiyong Cheng, Xiaojun Chang, Lei Zhu +2
Although the latent factor model achieves good accuracy in rating prediction, it suffers from many problems including cold-start, non-transparency, and suboptimal results for indiv…
Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews
Zhiyong Cheng, Ying Ding, Lei Zhu +1
Although latent factor models (e.g., matrix factorization) achieve good accuracy in rating prediction, they suffer from several problems including cold-start, non-transparency, and…