36 citations · 36 across the 1 of their papers we have counts for
4 papers
Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation
Bo Li, Yezhen Wang, Shanghang Zhang +4
The success of supervised learning hinges on the assumption that the training and test data come from the same underlying distribution, which is often not valid in practice due to…
ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation
Sicheng Zhao, Yezhen Wang, Bo Li +5
Due to its robust and precise distance measurements, LiDAR plays an important role in scene understanding for autonomous driving. Training deep neural networks (DNNs) on LiDAR data…
Rethinking Distributional Matching Based Domain Adaptation
Bo Li, Yezhen Wang, Tong Che +6
Domain adaptation (DA) is a technique that transfers predictive models trained on a labeled source domain to an unlabeled target domain, with the core difficulty of resolving distr…
Perspective-Guided Convolution Networks for Crowd Counting
Zhaoyi Yan, Yuchen Yuan, Wangmeng Zuo +4
In this paper, we propose a novel perspective-guided convolution (PGC) for convolutional neural network (CNN) based crowd counting (i.e. PGCNet), which aims to overcome the dramati…