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Faint Features Tell: Automatic Vertebrae Fracture Screening Assisted by Contrastive Learning


Aug 23, 2022
Xin Wei, Huaiwei Cong, Zheng Zhang, Junran Peng, Guoping Chen, Jinpeng Li


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Structure Regularized Attentive Network for Automatic Femoral Head Necrosis Diagnosis and Localization


Aug 23, 2022
Lingfeng Li, Huaiwei Cong, Gangming Zhao, Junran Peng, Zheng Zhang, Jinpeng Li


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Beyond Masking: Demystifying Token-Based Pre-Training for Vision Transformers


Mar 27, 2022
Yunjie Tian, Lingxi Xie, Jiemin Fang, Mengnan Shi, Junran Peng, Xiaopeng Zhang, Jianbin Jiao, Qi Tian, Qixiang Ye

* 20 pages, 5 figures, 3 tables 

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DATA: Domain-Aware and Task-Aware Self-supervised Learning


Mar 25, 2022
Qing Chang, Junran Peng, Lingxie Xie, Jiajun Sun, Haoran Yin, Qi Tian, Zhaoxiang Zhang

* CVPR 2022,8 pages,3 figures 

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GAIA: A Transfer Learning System of Object Detection that Fits Your Needs


Jun 21, 2021
Xingyuan Bu, Junran Peng, Junjie Yan, Tieniu Tan, Zhaoxiang Zhang

* CVPR2021. The first two authors contribute equally. Code is released at https://github.com/GAIA-vision 

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Large-Scale Object Detection in the Wild from Imbalanced Multi-Labels


May 18, 2020
Junran Peng, Xingyuan Bu, Ming Sun, Zhaoxiang Zhang, Tieniu Tan, Junjie Yan

* CVPR2020 oral. The first two authors contribute equally 

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Learning an Efficient Network for Large-Scale Hierarchical Object Detection with Data Imbalance: 3rd Place Solution to Open Images Challenge 2019


Oct 26, 2019
Xingyuan Bu, Junran Peng, Changbao Wang, Cunjun Yu, Guoliang Cao

* 6 pages, 3 figures, ICCV 2019 Open Images Workshop 

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Efficient Neural Architecture Transformation Searchin Channel-Level for Object Detection


Sep 05, 2019
Junran Peng, Ming Sun, Zhaoxiang Zhang, Tieniu Tan, Junjie Yan


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POD: Practical Object Detection with Scale-Sensitive Network


Sep 05, 2019
Junran Peng, Ming Sun, Zhaoxiang Zhang, Tieniu Tan, Junjie Yan


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Accelerating Deep Neural Networks with Spatial Bottleneck Modules


Sep 07, 2018
Junran Peng, Lingxi Xie, Zhaoxiang Zhang, Tieniu Tan, Jingdong Wang

* 9 pages, 5 figures 

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