9 citations · 27 across the 17 of their papers we have counts for
22 papers
Long-tail Cross Modal Hashing
Zijun Gao, Jun Wang, Guoxian Yu +3
Existing Cross Modal Hashing (CMH) methods are mainly designed for balanced data, while imbalanced data with long-tail distribution is more general in real-world. Several long-tail…
Reinforcement Causal Structure Learning on Order Graph
Dezhi Yang, Guoxian Yu, Jun Wang +2
Learning directed acyclic graph (DAG) that describes the causality of observed data is a very challenging but important task. Due to the limited quantity and quality of observed da…
MetaMIML: Meta Multi-Instance Multi-Label Learning
Yuanlin Yang, Guoxian Yu, Jun Wang +3
Multi-Instance Multi-Label learning (MIML) models complex objects (bags), each of which is associated with a set of interrelated labels and composed with a set of instances. Curren…
Meta Cross-Modal Hashing on Long-Tailed Data
Runmin Wang, Guoxian Yu, Carlotta Domeniconi +1
Due to the advantage of reducing storage while speeding up query time on big heterogeneous data, cross-modal hashing has been extensively studied for approximate nearest neighbor s…
Cross-modal Zero-shot Hashing by Label Attributes Embedding
Runmin Wang, Guoxian Yu, Lei Liu +3
Cross-modal hashing (CMH) is one of the most promising methods in cross-modal approximate nearest neighbor search. Most CMH solutions ideally assume the labels of training and test…
Open-Set Crowdsourcing using Multiple-Source Transfer Learning
Guangyang Han, Guoxian Yu, Lei Liu +3
We raise and define a new crowdsourcing scenario, open set crowdsourcing, where we only know the general theme of an unfamiliar crowdsourcing project, and we don't know its label s…