3 papers
cs.CL2023
Graph Contrastive Topic Model
Zheheng Luo, Lei Liu, Qianqian Xie +1
Existing NTMs with contrastive learning suffer from the sample bias problem owing to the word frequency-based sampling strategy, which may result in false negative samples with sim…
cs.LG2021
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…
cs.CV2021
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…