3 papers
cs.CV2020
An Empirical Study of the Collapsing Problem in Semi-Supervised 2D Human Pose Estimation
Rongchang Xie, Chunyu Wang, Wenjun Zeng +1
Semi-supervised learning aims to boost the accuracy of a model by exploring unlabeled images. The state-of-the-art methods are consistency-based which learn about unlabeled images…
cs.CV2020
MetaFuse: A Pre-trained Fusion Model for Human Pose Estimation
Rongchang Xie, Chunyu Wang, Yizhou Wang
Cross view feature fusion is the key to address the occlusion problem in human pose estimation. The current fusion methods need to train a separate model for every pair of cameras…
cs.CV2019
Multi-level Domain Adaptive learning for Cross-Domain Detection
Rongchang Xie, Fei Yu, Jiachao Wang +2
In recent years, object detection has shown impressive results using supervised deep learning, but it remains challenging in a cross-domain environment. The variations of illuminat…