11 citations · 15 across the 7 of their papers we have counts for
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cs.CV2022
Revisiting Pretraining for Semi-Supervised Learning in the Low-Label Regime
Xun Xu, Jingyi Liao, Lile Cai +5
Semi-supervised learning (SSL) addresses the lack of labeled data by exploiting large unlabeled data through pseudolabeling. However, in the extremely low-label regime, pseudo labe…
cs.CV2022
Open-Set Semi-Supervised Learning for 3D Point Cloud Understanding
Xian Shi, Xun Xu, Wanyue Zhang +3
Semantic understanding of 3D point cloud relies on learning models with massively annotated data, which, in many cases, are expensive or difficult to collect. This has led to an em…
cs.CV2022★ 3 cited
Few-Shot Adaptation of Pre-Trained Networks for Domain Shift
Wenyu Zhang, Li Shen, Wanyue Zhang +1
Deep networks are prone to performance degradation when there is a domain shift between the source (training) data and target (test) data. Recent test-time adaptation methods updat…