activity
20172022
most citedLabel Efficient Learning of Transferable Representations across Domains and Tasks

55 citations · 77 across the 4 of their papers we have counts for

collaborators

7 papers

cs.CV2022

Learning Instance-Specific Adaptation for Cross-Domain Segmentation

Yuliang Zou, Zizhao Zhang, Chun-Liang Li +3

We propose a test-time adaptation method for cross-domain image segmentation. Our method is simple: Given a new unseen instance at test time, we adapt a pre-trained model by conduc…

cs.CV2020

PseudoSeg: Designing Pseudo Labels for Semantic Segmentation

Yuliang Zou, Zizhao Zhang, Han Zhang +4

Recent advances in semi-supervised learning (SSL) demonstrate that a combination of consistency regularization and pseudo-labeling can effectively improve image classification accu…

cs.CV202019 cited

DRG: Dual Relation Graph for Human-Object Interaction Detection

Chen Gao, Jiarui Xu, Yuliang Zou +1

We tackle the challenging problem of human-object interaction (HOI) detection. Existing methods either recognize the interaction of each human-object pair in isolation or perform j…

cs.CV20203 cited

Learning Monocular Visual Odometry via Self-Supervised Long-Term Modeling

Yuliang Zou, Pan Ji, Quoc-Huy Tran +2

Monocular visual odometry (VO) suffers severely from error accumulation during frame-to-frame pose estimation. In this paper, we present a self-supervised learning method for VO wi…

cs.CV2018

DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistency

Yuliang Zou, Zelun Luo, Jia-Bin Huang

We present an unsupervised learning framework for simultaneously training single-view depth prediction and optical flow estimation models using unlabeled video sequences. Existing…

cs.CV2018

iCAN: Instance-Centric Attention Network for Human-Object Interaction Detection

Chen Gao, Yuliang Zou, Jia-Bin Huang

Recent years have witnessed rapid progress in detecting and recognizing individual object instances. To understand the situation in a scene, however, computers need to recognize ho…