164 citations · 445 across the 25 of their papers we have counts for
26 papers
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…
Hierarchical Memory Pool Based Edge Semi-Supervised Continual Learning Method
Xiangwei Wang, Rui Han, Chi Harold Liu
The continuous changes in the world have resulted in the performance regression of neural networks. Therefore, continual learning (CL) area gradually attracts the attention of more…
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…
FedKNOW: Federated Continual Learning with Signature Task Knowledge Integration at Edge
Yaxin Luopan, Rui Han, Qinglong Zhang +2
Deep Neural Networks (DNNs) have been ubiquitously adopted in internet of things and are becoming an integral of our daily life. When tackling the evolving learning tasks in real w…
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…
HiTailor: Interactive Transformation and Visualization for Hierarchical Tabular Data
Guozheng Li, Runfei Li, Zicheng Wang +3
Tabular visualization techniques integrate visual representations with tabular data to avoid additional cognitive load caused by splitting users' attention. However, most of the ex…
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…
Causality Inspired Representation Learning for Domain Generalization
Fangrui Lv, Jian Liang, Shuang Li +4
Domain generalization (DG) is essentially an out-of-distribution problem, aiming to generalize the knowledge learned from multiple source domains to an unseen target domain. The ma…
Pareto Domain Adaptation
Fangrui Lv, Jian Liang, Kaixiong Gong +5
Domain adaptation (DA) attempts to transfer the knowledge from a labeled source domain to an unlabeled target domain that follows different distribution from the source. To achieve…
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…
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…