Get our free extension to see links to code for papers anywhere online!

Chrome logo Add to Chrome

Firefox logo Add to Firefox
Picture for Bo Zhou

Bo Zhou

Reinforcement Learning with Evolutionary Trajectory Generator: A General Approach for Quadrupedal Locomotion


Sep 16, 2021
Haojie Shi, Bo Zhou, Hongsheng Zeng, Fan Wang, Yueqiang Dong, Jiangyong Li, Kang Wang, Hao Tian, Max Q. -H. Meng

Figure 1 for Reinforcement Learning with Evolutionary Trajectory Generator: A General Approach for Quadrupedal Locomotion
Figure 2 for Reinforcement Learning with Evolutionary Trajectory Generator: A General Approach for Quadrupedal Locomotion
Figure 3 for Reinforcement Learning with Evolutionary Trajectory Generator: A General Approach for Quadrupedal Locomotion
Figure 4 for Reinforcement Learning with Evolutionary Trajectory Generator: A General Approach for Quadrupedal Locomotion

  Access Paper or Ask Questions

ADER:Adapting between Exploration and Robustness for Actor-Critic Methods


Sep 08, 2021
Bo Zhou, Kejiao Li, Hongsheng Zeng, Fan Wang, Hao Tian

Figure 1 for ADER:Adapting between Exploration and Robustness for Actor-Critic Methods
Figure 2 for ADER:Adapting between Exploration and Robustness for Actor-Critic Methods
Figure 3 for ADER:Adapting between Exploration and Robustness for Actor-Critic Methods
Figure 4 for ADER:Adapting between Exploration and Robustness for Actor-Critic Methods

  Access Paper or Ask Questions

Synthesizing Multi-Tracer PET Images for Alzheimer's Disease Patients using a 3D Unified Anatomy-aware Cyclic Adversarial Network


Jul 12, 2021
Bo Zhou, Rui Wang, Ming-Kai Chen, Adam P. Mecca, Ryan S. O'Dell, Christopher H. Van Dyck, Richard E. Carson, James S. Duncan, Chi Liu

Figure 1 for Synthesizing Multi-Tracer PET Images for Alzheimer's Disease Patients using a 3D Unified Anatomy-aware Cyclic Adversarial Network
Figure 2 for Synthesizing Multi-Tracer PET Images for Alzheimer's Disease Patients using a 3D Unified Anatomy-aware Cyclic Adversarial Network
Figure 3 for Synthesizing Multi-Tracer PET Images for Alzheimer's Disease Patients using a 3D Unified Anatomy-aware Cyclic Adversarial Network
Figure 4 for Synthesizing Multi-Tracer PET Images for Alzheimer's Disease Patients using a 3D Unified Anatomy-aware Cyclic Adversarial Network
* Accepted at MICCAI 2021 
  Access Paper or Ask Questions

Anatomy-Constrained Contrastive Learning for Synthetic Segmentation without Ground-truth


Jul 12, 2021
Bo Zhou, Chi Liu, James S. Duncan

Figure 1 for Anatomy-Constrained Contrastive Learning for Synthetic Segmentation without Ground-truth
Figure 2 for Anatomy-Constrained Contrastive Learning for Synthetic Segmentation without Ground-truth
Figure 3 for Anatomy-Constrained Contrastive Learning for Synthetic Segmentation without Ground-truth
Figure 4 for Anatomy-Constrained Contrastive Learning for Synthetic Segmentation without Ground-truth
* Accepted at MICCAI 2021 
  Access Paper or Ask Questions

Action Set Based Policy Optimization for Safe Power Grid Management


Jun 29, 2021
Bo Zhou, Hongsheng Zeng, Yuecheng Liu, Kejiao Li, Fan Wang, Hao Tian

Figure 1 for Action Set Based Policy Optimization for Safe Power Grid Management
Figure 2 for Action Set Based Policy Optimization for Safe Power Grid Management
Figure 3 for Action Set Based Policy Optimization for Safe Power Grid Management
Figure 4 for Action Set Based Policy Optimization for Safe Power Grid Management
* accepted by ECML PKDD 2021; 1st place in NeurIPS2020 RL challenge: power grid management 
  Access Paper or Ask Questions

Anatomy-guided Multimodal Registration by Learning Segmentation without Ground Truth: Application to Intraprocedural CBCT/MR Liver Segmentation and Registration


Apr 14, 2021
Bo Zhou, Zachary Augenfeld, Julius Chapiro, S. Kevin Zhou, Chi Liu, James S. Duncan

Figure 1 for Anatomy-guided Multimodal Registration by Learning Segmentation without Ground Truth: Application to Intraprocedural CBCT/MR Liver Segmentation and Registration
Figure 2 for Anatomy-guided Multimodal Registration by Learning Segmentation without Ground Truth: Application to Intraprocedural CBCT/MR Liver Segmentation and Registration
Figure 3 for Anatomy-guided Multimodal Registration by Learning Segmentation without Ground Truth: Application to Intraprocedural CBCT/MR Liver Segmentation and Registration
Figure 4 for Anatomy-guided Multimodal Registration by Learning Segmentation without Ground Truth: Application to Intraprocedural CBCT/MR Liver Segmentation and Registration
* 12 pages, 8 figures, published at Medical Image Analysis (MedIA), code available at https://github.com/bbbbbbzhou/APA2Seg-Net 
  Access Paper or Ask Questions

Detecting Video Game Player Burnout with the Use of Sensor Data and Machine Learning


Nov 29, 2020
Anton Smerdov, Andrey Somov, Evgeny Burnaev, Bo Zhou, Paul Lukowicz

Figure 1 for Detecting Video Game Player Burnout with the Use of Sensor Data and Machine Learning
Figure 2 for Detecting Video Game Player Burnout with the Use of Sensor Data and Machine Learning
Figure 3 for Detecting Video Game Player Burnout with the Use of Sensor Data and Machine Learning
Figure 4 for Detecting Video Game Player Burnout with the Use of Sensor Data and Machine Learning

  Access Paper or Ask Questions

Collection and Validation of Psycophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset


Nov 02, 2020
Anton Smerdov, Bo Zhou, Paul Lukowicz, Andrey Somov

Figure 1 for Collection and Validation of Psycophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset
Figure 2 for Collection and Validation of Psycophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset
Figure 3 for Collection and Validation of Psycophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset
Figure 4 for Collection and Validation of Psycophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset

  Access Paper or Ask Questions

Long-distance tiny face detection based on enhanced YOLOv3 for unmanned system


Oct 09, 2020
Jia-Yi Chang, Yan-Feng Lu, Ya-Jun Liu, Bo Zhou, Hong Qiao

Figure 1 for Long-distance tiny face detection based on enhanced YOLOv3 for unmanned system
Figure 2 for Long-distance tiny face detection based on enhanced YOLOv3 for unmanned system
Figure 3 for Long-distance tiny face detection based on enhanced YOLOv3 for unmanned system
Figure 4 for Long-distance tiny face detection based on enhanced YOLOv3 for unmanned system

  Access Paper or Ask Questions

AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results


Sep 25, 2020
Pengxu Wei, Hannan Lu, Radu Timofte, Liang Lin, Wangmeng Zuo, Zhihong Pan, Baopu Li, Teng Xi, Yanwen Fan, Gang Zhang, Jingtuo Liu, Junyu Han, Errui Ding, Tangxin Xie, Liang Cao, Yan Zou, Yi Shen, Jialiang Zhang, Yu Jia, Kaihua Cheng, Chenhuan Wu, Yue Lin, Cen Liu, Yunbo Peng, Xueyi Zou, Zhipeng Luo, Yuehan Yao, Zhenyu Xu, Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Keon-Hee Ahn, Jun-Hyuk Kim, Jun-Ho Choi, Jong-Seok Lee, Tongtong Zhao, Shanshan Zhao, Yoseob Han, Byung-Hoon Kim, JaeHyun Baek, Haoning Wu, Dejia Xu, Bo Zhou, Wei Guan, Xiaobo Li, Chen Ye, Hao Li, Haoyu Zhong, Yukai Shi, Zhijing Yang, Xiaojun Yang, Haoyu Zhong, Xin Li, Xin Jin, Yaojun Wu, Yingxue Pang, Sen Liu, Zhi-Song Liu, Li-Wen Wang, Chu-Tak Li, Marie-Paule Cani, Wan-Chi Siu, Yuanbo Zhou, Rao Muhammad Umer, Christian Micheloni, Xiaofeng Cong, Rajat Gupta, Keon-Hee Ahn, Jun-Hyuk Kim, Jun-Ho Choi, Jong-Seok Lee, Feras Almasri, Thomas Vandamme, Olivier Debeir

Figure 1 for AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
Figure 2 for AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
Figure 3 for AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
Figure 4 for AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
* European Conference on Computer Vision Workshops, 2020 
  Access Paper or Ask Questions

Simultaneous Denoising and Motion Estimation for Low-dose Gated PET using a Siamese Adversarial Network with Gate-to-Gate Consistency Learning


Sep 14, 2020
Bo Zhou, Yu-Jung Tsai, Chi Liu

Figure 1 for Simultaneous Denoising and Motion Estimation for Low-dose Gated PET using a Siamese Adversarial Network with Gate-to-Gate Consistency Learning
Figure 2 for Simultaneous Denoising and Motion Estimation for Low-dose Gated PET using a Siamese Adversarial Network with Gate-to-Gate Consistency Learning
Figure 3 for Simultaneous Denoising and Motion Estimation for Low-dose Gated PET using a Siamese Adversarial Network with Gate-to-Gate Consistency Learning
Figure 4 for Simultaneous Denoising and Motion Estimation for Low-dose Gated PET using a Siamese Adversarial Network with Gate-to-Gate Consistency Learning
* Accepted at MICCAI 2020 
  Access Paper or Ask Questions

Limited View Tomographic Reconstruction Using a Deep Recurrent Framework with Residual Dense Spatial-Channel Attention Network and Sinogram Consistency


Sep 03, 2020
Bo Zhou, S. Kevin Zhou, James S. Duncan, Chi Liu

Figure 1 for Limited View Tomographic Reconstruction Using a Deep Recurrent Framework with Residual Dense Spatial-Channel Attention Network and Sinogram Consistency
Figure 2 for Limited View Tomographic Reconstruction Using a Deep Recurrent Framework with Residual Dense Spatial-Channel Attention Network and Sinogram Consistency
Figure 3 for Limited View Tomographic Reconstruction Using a Deep Recurrent Framework with Residual Dense Spatial-Channel Attention Network and Sinogram Consistency
Figure 4 for Limited View Tomographic Reconstruction Using a Deep Recurrent Framework with Residual Dense Spatial-Channel Attention Network and Sinogram Consistency
* Submitted to the IEEE for possible publication 
  Access Paper or Ask Questions

DeepFaceLab: A simple, flexible and extensible face swapping framework


May 20, 2020
Ivan Perov, Daiheng Gao, Nikolay Chervoniy, Kunlin Liu, Sugasa Marangonda, Chris UmΓ©, Mr. Dpfks, Carl Shift Facenheim, Luis RP, Jian Jiang, Sheng Zhang, Pingyu Wu, Bo Zhou, Weiming Zhang

Figure 1 for DeepFaceLab: A simple, flexible and extensible face swapping framework
Figure 2 for DeepFaceLab: A simple, flexible and extensible face swapping framework
Figure 3 for DeepFaceLab: A simple, flexible and extensible face swapping framework
Figure 4 for DeepFaceLab: A simple, flexible and extensible face swapping framework

  Access Paper or Ask Questions

A deep learning-facilitated radiomics solution for the prediction of lung lesion shrinkage in non-small cell lung cancer trials


Mar 05, 2020
Antong Chen, Jennifer Saouaf, Bo Zhou, Randolph Crawford, Jianda Yuan, Junshui Ma, Richard Baumgartner, Shubing Wang, Gregory Goldmacher

Figure 1 for A deep learning-facilitated radiomics solution for the prediction of lung lesion shrinkage in non-small cell lung cancer trials
Figure 2 for A deep learning-facilitated radiomics solution for the prediction of lung lesion shrinkage in non-small cell lung cancer trials
Figure 3 for A deep learning-facilitated radiomics solution for the prediction of lung lesion shrinkage in non-small cell lung cancer trials
Figure 4 for A deep learning-facilitated radiomics solution for the prediction of lung lesion shrinkage in non-small cell lung cancer trials
* Accepted by International Symposium on Biomedical Imaging (ISBI) 2020 
  Access Paper or Ask Questions

DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI Reconstruction with Deep T1 Prior


Jan 11, 2020
Bo Zhou, S. Kevin Zhou

Figure 1 for DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI Reconstruction with Deep T1 Prior
Figure 2 for DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI Reconstruction with Deep T1 Prior
Figure 3 for DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI Reconstruction with Deep T1 Prior
Figure 4 for DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI Reconstruction with Deep T1 Prior

  Access Paper or Ask Questions

Efficient and Robust Reinforcement Learning with Uncertainty-based Value Expansion


Dec 10, 2019
Bo Zhou, Hongsheng Zeng, Fan Wang, Yunxiang Li, Hao Tian

Figure 1 for Efficient and Robust Reinforcement Learning with Uncertainty-based Value Expansion
Figure 2 for Efficient and Robust Reinforcement Learning with Uncertainty-based Value Expansion
Figure 3 for Efficient and Robust Reinforcement Learning with Uncertainty-based Value Expansion
Figure 4 for Efficient and Robust Reinforcement Learning with Uncertainty-based Value Expansion
* 1st place in NeurIPS 2019: Learn to Move - Walk Around competition and best paper award 
  Access Paper or Ask Questions

CT Data Curation for Liver Patients: Phase Recognition in Dynamic Contrast-Enhanced CT


Sep 27, 2019
Bo Zhou, Adam P. Harrison, Jiawen Yao, Chi-Tung Cheng, Jing Xiao, Chien-Hung Liao, Le Lu

Figure 1 for CT Data Curation for Liver Patients: Phase Recognition in Dynamic Contrast-Enhanced CT
Figure 2 for CT Data Curation for Liver Patients: Phase Recognition in Dynamic Contrast-Enhanced CT
Figure 3 for CT Data Curation for Liver Patients: Phase Recognition in Dynamic Contrast-Enhanced CT
Figure 4 for CT Data Curation for Liver Patients: Phase Recognition in Dynamic Contrast-Enhanced CT
* 11 pages, accepted by 2019 MICCAI - Medical Image Learning with Less Labels and Imperfect Data Workshop 
  Access Paper or Ask Questions

Artificial Intelligence for Prosthetics - challenge solutions


Feb 07, 2019
Łukasz KidziΕ„ski, Carmichael Ong, Sharada Prasanna Mohanty, Jennifer Hicks, Sean F. Carroll, Bo Zhou, Hongsheng Zeng, Fan Wang, Rongzhong Lian, Hao Tian, Wojciech JaΕ›kowski, Garrett Andersen, Odd Rune LykkebΓΈ, Nihat Engin Toklu, Pranav Shyam, Rupesh Kumar Srivastava, Sergey Kolesnikov, Oleksii Hrinchuk, Anton Pechenko, Mattias LjungstrΓΆm, Zhen Wang, Xu Hu, Zehong Hu, Minghui Qiu, Jun Huang, Aleksei Shpilman, Ivan Sosin, Oleg Svidchenko, Aleksandra Malysheva, Daniel Kudenko, Lance Rane, Aditya Bhatt, Zhengfei Wang, Penghui Qi, Zeyang Yu, Peng Peng, Quan Yuan, Wenxin Li, Yunsheng Tian, Ruihan Yang, Pingchuan Ma, Shauharda Khadka, Somdeb Majumdar, Zach Dwiel, Yinyin Liu, Evren Tumer, Jeremy Watson, Marcel SalathΓ©, Sergey Levine, Scott Delp

Figure 1 for Artificial Intelligence for Prosthetics - challenge solutions
Figure 2 for Artificial Intelligence for Prosthetics - challenge solutions
Figure 3 for Artificial Intelligence for Prosthetics - challenge solutions
Figure 4 for Artificial Intelligence for Prosthetics - challenge solutions

  Access Paper or Ask Questions

Intervention Aided Reinforcement Learning for Safe and Practical Policy Optimization in Navigation


Nov 15, 2018
Fan Wang, Bo Zhou, Ke Chen, Tingxiang Fan, Xi Zhang, Jiangyong Li, Hao Tian, Jia Pan

Figure 1 for Intervention Aided Reinforcement Learning for Safe and Practical Policy Optimization in Navigation
Figure 2 for Intervention Aided Reinforcement Learning for Safe and Practical Policy Optimization in Navigation
Figure 3 for Intervention Aided Reinforcement Learning for Safe and Practical Policy Optimization in Navigation
Figure 4 for Intervention Aided Reinforcement Learning for Safe and Practical Policy Optimization in Navigation
* Wang, F., Zhou, B., Chen, K., Fan, T., Zhang, X., Li, J., ... & Pan, J. (2018, October). Intervention Aided Reinforcement Learning for Safe and Practical Policy Optimization in Navigation. In Conference on Robot Learning (pp. 410-421) 
  Access Paper or Ask Questions

A Progressively-trained Scale-invariant and Boundary-aware Deep Neural Network for the Automatic 3D Segmentation of Lung Lesions


Nov 11, 2018
Bo Zhou, Randolph Crawford, Belma Dogdas, Gregory Goldmacher, Antong Chen

Figure 1 for A Progressively-trained Scale-invariant and Boundary-aware Deep Neural Network for the Automatic 3D Segmentation of Lung Lesions
Figure 2 for A Progressively-trained Scale-invariant and Boundary-aware Deep Neural Network for the Automatic 3D Segmentation of Lung Lesions
Figure 3 for A Progressively-trained Scale-invariant and Boundary-aware Deep Neural Network for the Automatic 3D Segmentation of Lung Lesions
Figure 4 for A Progressively-trained Scale-invariant and Boundary-aware Deep Neural Network for the Automatic 3D Segmentation of Lung Lesions
* 10 pages, 7 figures, 3 tables, accepted by 2019 IEEE Winter Conference on Applications of Computer Vision (2019 WACV) 
  Access Paper or Ask Questions

A Weakly Supervised Adaptive DenseNet for Classifying Thoracic Diseases and Identifying Abnormalities


Nov 05, 2018
Bo Zhou, Yuemeng Li, Jiangcong Wang

Figure 1 for A Weakly Supervised Adaptive DenseNet for Classifying Thoracic Diseases and Identifying Abnormalities
Figure 2 for A Weakly Supervised Adaptive DenseNet for Classifying Thoracic Diseases and Identifying Abnormalities
Figure 3 for A Weakly Supervised Adaptive DenseNet for Classifying Thoracic Diseases and Identifying Abnormalities
Figure 4 for A Weakly Supervised Adaptive DenseNet for Classifying Thoracic Diseases and Identifying Abnormalities
* 10 pages, 6 figures; accepted by IEEE Winter Conference on Applications of Computer Vision (2019 WACV) 
  Access Paper or Ask Questions

Generation of Virtual Dual Energy Images from Standard Single-Shot Radiographs using Multi-scale and Conditional Adversarial Network


Oct 22, 2018
Bo Zhou, Xunyu Lin, Brendan Eck, Jun Hou, David Wilson

Figure 1 for Generation of Virtual Dual Energy Images from Standard Single-Shot Radiographs using Multi-scale and Conditional Adversarial Network
Figure 2 for Generation of Virtual Dual Energy Images from Standard Single-Shot Radiographs using Multi-scale and Conditional Adversarial Network
Figure 3 for Generation of Virtual Dual Energy Images from Standard Single-Shot Radiographs using Multi-scale and Conditional Adversarial Network
Figure 4 for Generation of Virtual Dual Energy Images from Standard Single-Shot Radiographs using Multi-scale and Conditional Adversarial Network
* 16 pages, 7 figures, accepted by Asian Conference on Computer Vision (2018 ACCV) 
  Access Paper or Ask Questions

Respond-CAM: Analyzing Deep Models for 3D Imaging Data by Visualizations


Jun 07, 2018
Guannan Zhao, Bo Zhou, Kaiwen Wang, Rui Jiang, Min Xu

Figure 1 for Respond-CAM: Analyzing Deep Models for 3D Imaging Data by Visualizations
Figure 2 for Respond-CAM: Analyzing Deep Models for 3D Imaging Data by Visualizations
Figure 3 for Respond-CAM: Analyzing Deep Models for 3D Imaging Data by Visualizations
Figure 4 for Respond-CAM: Analyzing Deep Models for 3D Imaging Data by Visualizations
* Medical Image Computing & Computer Assisted Intervention (MICCAI) 2018 
  Access Paper or Ask Questions

Model compression for faster structural separation of macromolecules captured by Cellular Electron Cryo-Tomography


Jan 31, 2018
Jialiang Guo, Bo Zhou, Xiangrui Zeng, Zachary Freyberg, Min Xu

Figure 1 for Model compression for faster structural separation of macromolecules captured by Cellular Electron Cryo-Tomography
Figure 2 for Model compression for faster structural separation of macromolecules captured by Cellular Electron Cryo-Tomography
Figure 3 for Model compression for faster structural separation of macromolecules captured by Cellular Electron Cryo-Tomography
Figure 4 for Model compression for faster structural separation of macromolecules captured by Cellular Electron Cryo-Tomography
* International Conference on Image Analysis and Recognition (ICIAR) 2018 
 * 8 pages 
  Access Paper or Ask Questions

Feature Decomposition Based Saliency Detection in Electron Cryo-Tomograms


Jan 31, 2018
Bo Zhou, Qiang Guo, Xiangrui Zeng, Min Xu

Figure 1 for Feature Decomposition Based Saliency Detection in Electron Cryo-Tomograms
Figure 2 for Feature Decomposition Based Saliency Detection in Electron Cryo-Tomograms
Figure 3 for Feature Decomposition Based Saliency Detection in Electron Cryo-Tomograms
Figure 4 for Feature Decomposition Based Saliency Detection in Electron Cryo-Tomograms
* 14 pages 
  Access Paper or Ask Questions

Simultaneously Learning Neighborship and Projection Matrix for Supervised Dimensionality Reduction


Sep 09, 2017
Yanwei Pang, Bo Zhou, Feiping Nie

Figure 1 for Simultaneously Learning Neighborship and Projection Matrix for Supervised Dimensionality Reduction
Figure 2 for Simultaneously Learning Neighborship and Projection Matrix for Supervised Dimensionality Reduction
Figure 3 for Simultaneously Learning Neighborship and Projection Matrix for Supervised Dimensionality Reduction
Figure 4 for Simultaneously Learning Neighborship and Projection Matrix for Supervised Dimensionality Reduction

  Access Paper or Ask Questions

Transforming Sensor Data to the Image Domain for Deep Learning - an Application to Footstep Detection


Jul 14, 2017
Monit Shah Singh, Vinaychandran Pondenkandath, Bo Zhou, Paul Lukowicz, Marcus Liwicki

Figure 1 for Transforming Sensor Data to the Image Domain for Deep Learning - an Application to Footstep Detection
Figure 2 for Transforming Sensor Data to the Image Domain for Deep Learning - an Application to Footstep Detection
Figure 3 for Transforming Sensor Data to the Image Domain for Deep Learning - an Application to Footstep Detection
Figure 4 for Transforming Sensor Data to the Image Domain for Deep Learning - an Application to Footstep Detection
* 8 pages, 8 figures, Published in IJCNN, 2017 Copyright: IEEE 2017 DOI: 10.1109/IJCNN.2017.7966182 
  Access Paper or Ask Questions