276 citations · 345 across the 10 of their papers we have counts for
6 papers · 1 filter
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…
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…
Semi-supervised classification of radiology images with NoTeacher: A Teacher that is not Mean
Balagopal Unnikrishnan, Cuong Nguyen, Shafa Balaram +3
Deep learning models achieve strong performance for radiology image classification, but their practical application is bottlenecked by the need for large labeled training datasets.…
Label-Efficient Point Cloud Semantic Segmentation: An Active Learning Approach
Xian Shi, Xun Xu, Ke Chen +3
Deep learning models are the state-of-the-art methods for semantic point cloud segmentation, the success of which relies on the availability of large-scale annotated datasets. Howe…
Semi-Supervised Deep Learning for Abnormality Classification in Retinal Images
Bruno Lecouat, Ken Chang, Chuan-Sheng Foo +7
Supervised deep learning algorithms have enabled significant performance gains in medical image classification tasks. But these methods rely on large labeled datasets that require…
Holistic Multi-modal Memory Network for Movie Question Answering
Anran Wang, Anh Tuan Luu, Chuan-Sheng Foo +3
Answering questions according to multi-modal context is a challenging problem as it requires a deep integration of different data sources. Existing approaches only employ partial i…