activity
20182022
most citedUnsupervised Adversarial Graph Alignment with Graph Embedding

5 citations · 15 across the 5 of their papers we have counts for

collaborators

8 papers

cs.CV20221 cited

Compound Domain Generalization via Meta-Knowledge Encoding

Chaoqi Chen, Jiongcheng Li, Xiaoguang Han +2

Domain generalization (DG) aims to improve the generalization performance for an unseen target domain by using the knowledge of multiple seen source domains. Mainstream DG methods…

cs.CV20213 cited

Act Like a Radiologist: Towards Reliable Multi-view Correspondence Reasoning for Mammogram Mass Detection

Yuhang Liu, Fandong Zhang, Chaoqi Chen +3

Mammogram mass detection is crucial for diagnosing and preventing the breast cancers in clinical practice. The complementary effect of multi-view mammogram images provides valuable…

cs.CV20215 cited

I3Net: Implicit Instance-Invariant Network for Adapting One-Stage Object Detectors

Chaoqi Chen, Zebiao Zheng, Yue Huang +2

Recent works on two-stage cross-domain detection have widely explored the local feature patterns to achieve more accurate adaptation results. These methods heavily rely on the regi…

cs.CV2020

Hard Class Rectification for Domain Adaptation

Yunlong Zhang, Changxing Jing, Huangxing Lin +4

Domain adaptation (DA) aims to transfer knowledge from a label-rich and related domain (source domain) to a label-scare domain (target domain). Pseudo-labeling has recently been wi…

cs.CV2020

Harmonizing Transferability and Discriminability for Adapting Object Detectors

Chaoqi Chen, Zebiao Zheng, Xinghao Ding +2

Recent advances in adaptive object detection have achieved compelling results in virtue of adversarial feature adaptation to mitigate the distributional shifts along the detection…

eess.IV20191 cited

Multi-sequence Cardiac MR Segmentation with Adversarial Domain Adaptation Network

Jiexiang Wang, Hongyu Huang, Chaoqi Chen +3

Automatic and accurate segmentation of the ventricles and myocardium from multi-sequence cardiac MRI (CMR) is crucial for the diagnosis and treatment management for patients suffer…