643 citations · 716 across the 9 of their papers we have counts for
9 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…
CoinSeg: Contrast Inter- and Intra- Class Representations for Incremental Segmentation
Zekang Zhang, Guangyu Gao, Jianbo Jiao +2
Class incremental semantic segmentation aims to strike a balance between the model's stability and plasticity by maintaining old knowledge while adapting to new concepts. However,…
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…
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…