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

no code implementations • 6 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 +5

Compound Domain Generalization via Meta-Knowledge Encoding

no code implementations • CVPR 2022 • Chaoqi Chen, Jiongcheng Li, Xiaoguang Han, Xiaoqing Liu, Yizhou Yu

Such holistic semantic structure, referred to as meta-knowledge here, is crucial for learning generalizable representations.

Domain Generalization Out-of-Distribution Generalization

GAMMA Challenge:Glaucoma grAding from Multi-Modality imAges

no code implementations • 14 Feb 2022 • Junde Wu, Huihui Fang, Fei Li, Huazhu Fu, Fengbin Lin, Jiongcheng Li, Lexing Huang, Qinji Yu, Sifan Song, Xinxing Xu, Yanyu Xu, Wensai Wang, Lingxiao Wang, Shuai Lu, Huiqi Li, Shihua Huang, Zhichao Lu, Chubin Ou, Xifei Wei, Bingyuan Liu, Riadh Kobbi, Xiaoying Tang, Li Lin, Qiang Zhou, Qiang Hu, Hrvoje Bogunovic, José Ignacio Orlando, Xiulan Zhang, Yanwu Xu

However, although numerous algorithms are proposed based on fundus images or OCT volumes in computer-aided diagnosis, there are still few methods leveraging both of the modalities for the glaucoma assessment.

Unsupervised Anomaly Segmentation using Image-Semantic Cycle Translation

no code implementations • 16 Mar 2021 • Chenxin Li, Yunlong Zhang, Jiongcheng Li, Yue Huang, Xinghao Ding

In this paper, to alleviate this issue, we introduce the semantic space of healthy anatomy in the process of modeling healthy-data distribution.

Anatomy Anomaly Detection +2

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

AGE Challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomography

no code implementations • 5 May 2020 • Huazhu Fu, Fei Li, Xu sun, Xingxing Cao, Jingan Liao, Jose Ignacio Orlando, Xing Tao, Yuexiang Li, Shihao Zhang, Mingkui Tan, Chenglang Yuan, Cheng Bian, Ruitao Xie, Jiongcheng Li, Xiaomeng Li, Jing Wang, Le Geng, Panming Li, Huaying Hao, Jiang Liu, Yan Kong, Yongyong Ren, Hrvoje Bogunovic, Xiulan Zhang, Yanwu Xu

To address this, we organized the Angle closure Glaucoma Evaluation challenge (AGE), held in conjunction with MICCAI 2019.