13 citations · 37 across the 6 of their papers we have counts for
4 papers · 1 filter
T-SVDNet: Exploring High-Order Prototypical Correlations for Multi-Source Domain Adaptation
Ruihuang Li, Xu Jia, Jianzhong He +2
Most existing domain adaptation methods focus on adaptation from only one source domain, however, in practice there are a number of relevant sources that could be leveraged to help…
Multi-Target Domain Adaptation with Collaborative Consistency Learning
Takashi Isobe, Xu Jia, Shuaijun Chen +5
Recently unsupervised domain adaptation for the semantic segmentation task has become more and more popular due to high-cost of pixel-level annotation on real-world images. However…
Semi-supervised Domain Adaptation based on Dual-level Domain Mixing for Semantic Segmentation
Shuaijun Chen, Xu Jia, Jianzhong He +2
Data-driven based approaches, in spite of great success in many tasks, have poor generalization when applied to unseen image domains, and require expensive cost of annotation espec…
Multi-Source Domain Adaptation with Collaborative Learning for Semantic Segmentation
Jianzhong He, Xu Jia, Shuaijun Chen +1
Multi-source unsupervised domain adaptation~(MSDA) aims at adapting models trained on multiple labeled source domains to an unlabeled target domain. In this paper, we propose a nov…