Search Results for author: Chaoqi Chen

Found 10 papers, 3 papers with code

Relation Matters: Foreground-aware Graph-based Relational Reasoning for Domain Adaptive Object Detection

no code implementations6 Jun 2022 Chaoqi Chen, Jiongcheng Li, Hong-Yu Zhou, Xiaoguang Han, Yue Huang, Xinghao Ding, Yizhou Yu

However, both the global and local alignment approaches fail to capture the topological relations among different foreground objects as the explicit dependencies and interactions between and within domains are neglected.

Domain Adaptation Graph Attention +4

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

no code implementations21 May 2021 Yuhang Liu, Fandong Zhang, Chaoqi Chen, Siwen Wang, Yizhou Wang, Yizhou Yu

In this paper, we propose an Anatomy-aware Graph convolutional Network (AGN), which is tailored for mammogram mass detection and endows existing detection methods with multi-view reasoning ability.

Decision Making object-detection +1

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

1 code implementation CVPR 2021 Chaoqi Chen, Zebiao Zheng, Yue Huang, Xinghao Ding, Yizhou Yu

Motivated by this, we propose an Implicit Instance-Invariant Network (I3Net), which is tailored for adapting one-stage detectors and implicitly learns instance-invariant features via exploiting the natural characteristics of deep features in different layers.

Region Proposal

Dual Bipartite Graph Learning: A General Approach for Domain Adaptive Object Detection

no code implementations ICCV 2021 Chaoqi Chen, Jiongcheng Li, Zebiao Zheng, Yue Huang, Xinghao Ding, Yizhou Yu

Domain Adaptive Object Detection (DAOD) relieves the reliance on large-scale annotated data by transferring the knowledge learned from a labeled source domain to a new unlabeled target domain.

Domain Adaptation Graph Learning +2

Hard Class Rectification for Domain Adaptation

1 code implementation8 Aug 2020 Yunlong Zhang, Changxing Jing, Huangxing Lin, Chaoqi Chen, Yue Huang, Xinghao Ding, Yang Zou

Second, we further consider that the predictions of target samples belonging to the hard class are vulnerable to perturbations.

Unsupervised Domain Adaptation

Harmonizing Transferability and Discriminability for Adapting Object Detectors

1 code implementation CVPR 2020 Chaoqi Chen, Zebiao Zheng, Xinghao Ding, Yue Huang, Qi Dou

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

object-detection Weakly Supervised Object Detection

Multi-sequence Cardiac MR Segmentation with Adversarial Domain Adaptation Network

no code implementations28 Oct 2019 Jiexiang Wang, Hongyu Huang, Chaoqi Chen, Wenao Ma, Yue Huang, Xinghao Ding

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 suffering from myocardial infarction (MI).

Domain Adaptation

Unsupervised Adversarial Graph Alignment with Graph Embedding

no code implementations1 Jul 2019 Chaoqi Chen, Weiping Xie, Tingyang Xu, Yu Rong, Wenbing Huang, Xinghao Ding, Yue Huang, Junzhou Huang

In this paper, we propose an Unsupervised Adversarial Graph Alignment (UAGA) framework to learn a cross-graph alignment between two embedding spaces of different graphs in a fully unsupervised fashion (\emph{i. e.,} no existing anchor links and no users' personal profile or attribute information is available).

Graph Embedding Link Prediction

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