3 citations · 6 across the 4 of their papers we have counts for
5 papers · 1 filter
Multi-Anchor Active Domain Adaptation for Semantic Segmentation
Munan Ning, Donghuan Lu, Dong Wei +5
Unsupervised domain adaption has proven to be an effective approach for alleviating the intensive workload of manual annotation by aligning the synthetic source-domain data and the…
A New Bidirectional Unsupervised Domain Adaptation Segmentation Framework
Munan Ning, Cheng Bian, Dong Wei +5
Domain shift happens in cross-domain scenarios commonly because of the wide gaps between different domains: when applying a deep learning model well-trained in one domain to anothe…
A Macro-Micro Weakly-supervised Framework for AS-OCT Tissue Segmentation
Munan Ning, Cheng Bian, Donghuan Lu +7
Primary angle closure glaucoma (PACG) is the leading cause of irreversible blindness among Asian people. Early detection of PACG is essential, so as to provide timely treatment and…
Hierarchical Clustering with Hard-batch Triplet Loss for Person Re-identification
Kaiwei Zeng
For most unsupervised person re-identification (re-ID), people often adopt unsupervised domain adaptation (UDA) method. UDA often train on the labeled source dataset and evaluate o…
Energy Clustering for Unsupervised Person Re-identification
Kaiwei Zeng
Due to the high cost of data annotation in supervised learning for person re-identification (Re-ID) methods, unsupervised learning becomes more attractive in the real world. The Bo…