281 citations · 665 across the 22 of their papers we have counts for
9 papers · 1 filter
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…