4 citations · 15 across the 6 of their papers we have counts for
6 papers
Exploring Semantic Attributes from A Foundation Model for Federated Learning of Disjoint Label Spaces
Shitong Sun, Chenyang Si, Guile Wu +1
Conventional centralised deep learning paradigms are not feasible when data from different sources cannot be shared due to data privacy or transmission limitation. To resolve this…
Self-Supervised Consistent Quantization for Fully Unsupervised Image Retrieval
Guile Wu, Chao Zhang, Stephan Liwicki
Unsupervised image retrieval aims to learn an efficient retrieval system without expensive data annotations, but most existing methods rely heavily on handcrafted feature descripto…
Learning Unbiased Transferability for Domain Adaptation by Uncertainty Modeling
Jian Hu, Haowen Zhong, Junchi Yan +3
Domain adaptation (DA) aims to transfer knowledge learned from a labeled source domain to an unlabeled or a less labeled but related target domain. Ideally, the source and target d…
Decentralised Person Re-Identification with Selective Knowledge Aggregation
Shitong Sun, Guile Wu, Shaogang Gong
Existing person re-identification (Re-ID) methods mostly follow a centralised learning paradigm which shares all training data to a collection for model learning. This paradigm is…
Decentralised Learning from Independent Multi-Domain Labels for Person Re-Identification
Guile Wu, Shaogang Gong
Deep learning has been successful for many computer vision tasks due to the availability of shared and centralised large-scale training data. However, increasing awareness of priva…
Peer Collaborative Learning for Online Knowledge Distillation
Guile Wu, Shaogang Gong
Traditional knowledge distillation uses a two-stage training strategy to transfer knowledge from a high-capacity teacher model to a compact student model, which relies heavily on t…