164 citations · 311 across the 13 of their papers we have counts for
13 papers
Domain Adaptation via Prompt Learning
Chunjiang Ge, Rui Huang, Mixue Xie +4
Unsupervised domain adaption (UDA) aims to adapt models learned from a well-annotated source domain to a target domain, where only unlabeled samples are given. Current UDA approach…
Active Learning for Domain Adaptation: An Energy-Based Approach
Binhui Xie, Longhui Yuan, Shuang Li +3
Unsupervised domain adaptation has recently emerged as an effective paradigm for generalizing deep neural networks to new target domains. However, there is still enormous potential…
Exploiting Both Domain-specific and Invariant Knowledge via a Win-win Transformer for Unsupervised Domain Adaptation
Wenxuan Ma, Jinming Zhang, Shuang Li +3
Unsupervised Domain Adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain. Most existing UDA approaches enable knowledge transfer v…
SPCL: A New Framework for Domain Adaptive Semantic Segmentation via Semantic Prototype-based Contrastive Learning
Binhui Xie, Mingjia Li, Shuang Li
Although there is significant progress in supervised semantic segmentation, it remains challenging to deploy the segmentation models to unseen domains due to domain biases. Domain…
Semantic Distribution-aware Contrastive Adaptation for Semantic Segmentation
Shuang Li, Binhui Xie, Bin Zang +4
Domain adaptive semantic segmentation refers to making predictions on a certain target domain with only annotations of a specific source domain. Current state-of-the-art works sugg…
MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition
Shuang Li, Kaixiong Gong, Chi Harold Liu +3
Real-world training data usually exhibits long-tailed distribution, where several majority classes have a significantly larger number of samples than the remaining minority classes…
Dynamic Domain Adaptation for Efficient Inference
Shuang Li, Jinming Zhang, Wenxuan Ma +2
Domain adaptation (DA) enables knowledge transfer from a labeled source domain to an unlabeled target domain by reducing the cross-domain distribution discrepancy. Most prior DA ap…
Transferable Semantic Augmentation for Domain Adaptation
Shuang Li, Mixue Xie, Kaixiong Gong +3
Domain adaptation has been widely explored by transferring the knowledge from a label-rich source domain to a related but unlabeled target domain. Most existing domain adaptation a…
Generalized Domain Conditioned Adaptation Network
Shuang Li, Binhui Xie, Qiuxia Lin +3
Domain Adaptation (DA) attempts to transfer knowledge learned in the labeled source domain to the unlabeled but related target domain without requiring large amounts of target supe…
Bi-Classifier Determinacy Maximization for Unsupervised Domain Adaptation
Shuang Li, Fangrui Lv, Binhui Xie +3
Unsupervised domain adaptation challenges the problem of transferring knowledge from a well-labelled source domain to an unlabelled target domain. Recently,adversarial learning wit…
Domain Agnostic Learning for Unbiased Authentication
Jian Liang, Yuren Cao, Shuang Li +4
Authentication is the task of confirming the matching relationship between a data instance and a given identity. Typical examples of authentication problems include face recognitio…
Domain Conditioned Adaptation Network
Shuang Li, Chi Harold Liu, Qiuxia Lin +4
Tremendous research efforts have been made to thrive deep domain adaptation (DA) by seeking domain-invariant features. Most existing deep DA models only focus on aligning feature r…
Deep Residual Correction Network for Partial Domain Adaptation
Shuang Li, Chi Harold Liu, Qiuxia Lin +4
Deep domain adaptation methods have achieved appealing performance by learning transferable representations from a well-labeled source domain to a different but related unlabeled t…