90 citations · 136 across the 5 of their papers we have counts for
6 papers
A Smart Classroom Behavior Analysis Framework with a New Highly Congested Classroom Dataset
Wei Xu, Maoxiang Chu, Yuelong Fan +5
Student behavior detection is important for intelligent classroom analysis but remains challenging in large-class scenarios due to dense instance co-occurrence, asymmetric occlusio…
SSB: Simple but Strong Baseline for Boosting Performance of Open-Set Semi-Supervised Learning
Yue Fan, Anna Kukleva, Dengxin Dai +1
Semi-supervised learning (SSL) methods effectively leverage unlabeled data to improve model generalization. However, SSL models often underperform in open-set scenarios, where unla…
SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learning
Hao Chen, Ran Tao, Yue Fan +6
The critical challenge of Semi-Supervised Learning (SSL) is how to effectively leverage the limited labeled data and massive unlabeled data to improve the model's generalization pe…
An Embarrassingly Simple Baseline for Imbalanced Semi-Supervised Learning
Hao Chen, Yue Fan, Yidong Wang +5
Semi-supervised learning (SSL) has shown great promise in leveraging unlabeled data to improve model performance. While standard SSL assumes uniform data distribution, we consider…
USB: A Unified Semi-supervised Learning Benchmark for Classification
Yidong Wang, Hao Chen, Yue Fan +19
Semi-supervised learning (SSL) improves model generalization by leveraging massive unlabeled data to augment limited labeled samples. However, currently, popular SSL evaluation pro…
Tag2Vec: Learning Tag Representations in Tag Networks
Junshan Wang, Zhicong Lu, Guojie Song +3
Network embedding is a method to learn low-dimensional representation vectors for nodes in complex networks. In real networks, nodes may have multiple tags but existing methods ign…