activity
20192022
most citedMulti-source Domain Adaptation for Semantic Segmentation

80 citations · 93 across the 4 of their papers we have counts for

collaborators

5 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.CV20202 cited

An End-to-End Visual-Audio Attention Network for Emotion Recognition in User-Generated Videos

Sicheng Zhao, Yunsheng Ma, Yang Gu +6

Emotion recognition in user-generated videos plays an important role in human-centered computing. Existing methods mainly employ traditional two-stage shallow pipeline, i.e. extrac…

cs.LG20197 cited

Multi-source Distilling Domain Adaptation

Sicheng Zhao, Guangzhi Wang, Shanghang Zhang +7

Deep neural networks suffer from performance decay when there is domain shift between the labeled source domain and unlabeled target domain, which motivates the research on domain…

cs.CV201980 cited

Multi-source Domain Adaptation for Semantic Segmentation

Sicheng Zhao, Bo Li, Xiangyu Yue +5

Simulation-to-real domain adaptation for semantic segmentation has been actively studied for various applications such as autonomous driving. Existing methods mainly focus on a sin…

cs.CV2019

ROAM: Recurrently Optimizing Tracking Model

Tianyu Yang, Pengfei Xu, Runbo Hu +2

In this paper, we design a tracking model consisting of response generation and bounding box regression, where the first component produces a heat map to indicate the presence of t…