activity
20192024
most citedMulti-source Domain Adaptation for Semantic Segmentation

80 citations · 222 across the 12 of their papers we have counts for

collaborators

14 papers

cs.CV20224 cited

S4OD: Semi-Supervised learning for Single-Stage Object Detection

Yueming Zhang, Xingxu Yao, Chao Liu +7

Single-stage detectors suffer from extreme foreground-background class imbalance, while two-stage detectors do not. Therefore, in semi-supervised object detection, two-stage detect…

cs.CV2021

Multi-Source Domain Adaptation for Object Detection

Xingxu Yao, Sicheng Zhao, Pengfei Xu +1

To reduce annotation labor associated with object detection, an increasing number of studies focus on transferring the learned knowledge from a labeled source domain to another unl…

cs.CV20216 cited

2nd Place Solution for Waymo Open Dataset Challenge -- Real-time 2D Object Detection

Yueming Zhang, Xiaolin Song, Bing Bai +8

In an autonomous driving system, it is essential to recognize vehicles, pedestrians and cyclists from images. Besides the high accuracy of the prediction, the requirement of real-t…

cs.CV20203 cited

Emotional Semantics-Preserved and Feature-Aligned CycleGAN for Visual Emotion Adaptation

Sicheng Zhao, Xuanbai Chen, Xiangyu Yue +7

Thanks to large-scale labeled training data, deep neural networks (DNNs) have obtained remarkable success in many vision and multimedia tasks. However, because of the presence of d…

cs.CL20202 cited

Curriculum CycleGAN for Textual Sentiment Domain Adaptation with Multiple Sources

Sicheng Zhao, Yang Xiao, Jiang Guo +5

Sentiment analysis of user-generated reviews or comments on products and services in social networks can help enterprises to analyze the feedback from customers and take correspond…

cs.CV2020

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…