9 citations · 14 across the 4 of their papers we have counts for
4 papers
Enhancing Continuous Domain Adaptation with Multi-Path Transfer Curriculum
Hanbing Liu, Jingge Wang, Xuan Zhang +2
Addressing the large distribution gap between training and testing data has long been a challenge in machine learning, giving rise to fields such as transfer learning and domain ad…
Combating Noise: Semi-supervised Learning by Region Uncertainty Quantification
Zhenyu Wang, Yali Li, Ye Guo +1
Semi-supervised learning aims to leverage a large amount of unlabeled data for performance boosting. Existing works primarily focus on image classification. In this paper, we delve…
Data-Uncertainty Guided Multi-Phase Learning for Semi-Supervised Object Detection
Zhenyu Wang, Yali Li, Ye Guo +2
In this paper, we delve into semi-supervised object detection where unlabeled images are leveraged to break through the upper bound of fully-supervised object detection models. Pre…
CS-R-FCN: Cross-supervised Learning for Large-Scale Object Detection
Ye Guo, Yali Li, Shengjin Wang
Generic object detection is one of the most fundamental problems in computer vision, yet it is difficult to provide all the bounding-box-level annotations aiming at large-scale obj…