activity
20172022
most citedComplementary Pseudo Labels For Unsupervised Domain Adaptation On Person Re-identification

87 citations · 180 across the 7 of their papers we have counts for

collaborators

13 papers

cs.CV20222 cited

Boosting Semi-Supervised 3D Object Detection with Semi-Sampling

Xiaopei Wu, Yang Zhao, Liang Peng +6

Current 3D object detection methods heavily rely on an enormous amount of annotations. Semi-supervised learning can be used to alleviate this issue. Previous semi-supervised 3D obj…

cs.CV2021

Suppress-and-Refine Framework for End-to-End 3D Object Detection

Zili Liu, Guodong Xu, Honghui Yang +5

3D object detector based on Hough voting achieves great success and derives many follow-up works. Despite constantly refreshing the detection accuracy, these works suffer from hand…

cs.CV202187 cited

Complementary Pseudo Labels For Unsupervised Domain Adaptation On Person Re-identification

Hao Feng, Minghao Chen, Jinming Hu +3

In recent years, supervised person re-identification (re-ID) models have received increasing studies. However, these models trained on the source domain always suffer dramatic perf…

cs.CV2020

RESA: Recurrent Feature-Shift Aggregator for Lane Detection

Tu Zheng, Hao Fang, Yi Zhang +4

Lane detection is one of the most important tasks in self-driving. Due to various complex scenarios (e.g., severe occlusion, ambiguous lanes, etc.) and the sparse supervisory signa…

cs.LG2020

Part-dependent Label Noise: Towards Instance-dependent Label Noise

Xiaobo Xia, Tongliang Liu, Bo Han +6

Learning with the \textit{instance-dependent} label noise is challenging, because it is hard to model such real-world noise. Note that there are psychological and physiological evi…

cs.CV2020

Boundary-Aware Dense Feature Indicator for Single-Stage 3D Object Detection from Point Clouds

Guodong Xu, Wenxiao Wang, Zili Liu +4

3D object detection based on point clouds has become more and more popular. Some methods propose localizing 3D objects directly from raw point clouds to avoid information loss. How…