164 citations · 445 across the 25 of their papers we have counts for
20 papers · 1 filter
Annotator: A Generic Active Learning Baseline for LiDAR Semantic Segmentation
Binhui Xie, Shuang Li, Qingju Guo +2
Active learning, a label-efficient paradigm, empowers models to interactively query an oracle for labeling new data. In the realm of LiDAR semantic segmentation, the challenges ste…
Dirichlet-based Uncertainty Calibration for Active Domain Adaptation
Mixue Xie, Shuang Li, Rui Zhang +1
Active domain adaptation (DA) aims to maximally boost the model adaptation on a new target domain by actively selecting limited target data to annotate, whereas traditional active…
VBLC: Visibility Boosting and Logit-Constraint Learning for Domain Adaptive Semantic Segmentation under Adverse Conditions
Mingjia Li, Binhui Xie, Shuang Li +2
Generalizing models trained on normal visual conditions to target domains under adverse conditions is demanding in the practical systems. One prevalent solution is to bridge the do…
Making the Best of Both Worlds: A Domain-Oriented Transformer for Unsupervised Domain Adaptation
Wenxuan Ma, Jinming Zhang, Shuang Li +3
Extensive studies on Unsupervised Domain Adaptation (UDA) have propelled the deployment of deep learning from limited experimental datasets into real-world unconstrained domains. M…
ROMA: Cross-Domain Region Similarity Matching for Unpaired Nighttime Infrared to Daytime Visible Video Translation
Zhenjie Yu, Kai Chen, Shuang Li +3
Infrared cameras are often utilized to enhance the night vision since the visible light cameras exhibit inferior efficacy without sufficient illumination. However, infrared data po…
Improving Transferability for Domain Adaptive Detection Transformers
Kaixiong Gong, Shuang Li, Shugang Li +3
DETR-style detectors stand out amongst in-domain scenarios, but their properties in domain shift settings are under-explored. This paper aims to build a simple but effective baseli…
SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentation
Binhui Xie, Shuang Li, Mingjia Li +3
Domain adaptive semantic segmentation attempts to make satisfactory dense predictions on an unlabeled target domain by utilizing the supervised model trained on a labeled source do…
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…
Towards Fewer Annotations: Active Learning via Region Impurity and Prediction Uncertainty for Domain Adaptive Semantic Segmentation
Binhui Xie, Longhui Yuan, Shuang Li +2
Self-training has greatly facilitated domain adaptive semantic segmentation, which iteratively generates pseudo labels on unlabeled target data and retrains the network. However, r…
Semantic Concentration for Domain Adaptation
Shuang Li, Mixue Xie, Fangrui Lv +4
Domain adaptation (DA) paves the way for label annotation and dataset bias issues by the knowledge transfer from a label-rich source domain to a related but unlabeled target domain…
I2V-GAN: Unpaired Infrared-to-Visible Video Translation
Shuang Li, Bingfeng Han, Zhenjie Yu +3
Human vision is often adversely affected by complex environmental factors, especially in night vision scenarios. Thus, infrared cameras are often leveraged to help enhance the visu…
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…
Simultaneous Semantic Alignment Network for Heterogeneous Domain Adaptation
Shuang Li, Binhui Xie, Jiashu Wu +3
Heterogeneous domain adaptation (HDA) transfers knowledge across source and target domains that present heterogeneities e.g., distinct domain distributions and difference in featur…
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…