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 Hongyang Li

Hongyang Li

Sparse Steerable Convolutions: An Efficient Learning of SE(3)-Equivariant Features for Estimation and Tracking of Object Poses in 3D Space


Nov 14, 2021
Jiehong Lin, Hongyang Li, Ke Chen, Jiangbo Lu, Kui Jia

Figure 1 for Sparse Steerable Convolutions: An Efficient Learning of SE(3)-Equivariant Features for Estimation and Tracking of Object Poses in 3D Space
Figure 2 for Sparse Steerable Convolutions: An Efficient Learning of SE(3)-Equivariant Features for Estimation and Tracking of Object Poses in 3D Space
Figure 3 for Sparse Steerable Convolutions: An Efficient Learning of SE(3)-Equivariant Features for Estimation and Tracking of Object Poses in 3D Space
Figure 4 for Sparse Steerable Convolutions: An Efficient Learning of SE(3)-Equivariant Features for Estimation and Tracking of Object Poses in 3D Space
* Accepted by NeurIPS 2021 
  Access Paper or Ask Questions

Monocular 3D Object Detection: An Extrinsic Parameter Free Approach


Jun 30, 2021
Yunsong Zhou, Yuan He, Hongzi Zhu, Cheng Wang, Hongyang Li, Qinhong Jiang

Figure 1 for Monocular 3D Object Detection: An Extrinsic Parameter Free Approach
Figure 2 for Monocular 3D Object Detection: An Extrinsic Parameter Free Approach
Figure 3 for Monocular 3D Object Detection: An Extrinsic Parameter Free Approach
Figure 4 for Monocular 3D Object Detection: An Extrinsic Parameter Free Approach

  Access Paper or Ask Questions

Towards Improving the Consistency, Efficiency, and Flexibility of Differentiable Neural Architecture Search


Jan 27, 2021
Yibo Yang, Shan You, Hongyang Li, Fei Wang, Chen Qian, Zhouchen Lin

Figure 1 for Towards Improving the Consistency, Efficiency, and Flexibility of Differentiable Neural Architecture Search
Figure 2 for Towards Improving the Consistency, Efficiency, and Flexibility of Differentiable Neural Architecture Search
Figure 3 for Towards Improving the Consistency, Efficiency, and Flexibility of Differentiable Neural Architecture Search
Figure 4 for Towards Improving the Consistency, Efficiency, and Flexibility of Differentiable Neural Architecture Search

  Access Paper or Ask Questions

Exploring Intermediate Representation for Monocular Vehicle Pose Estimation


Nov 17, 2020
Shichao Li, Zengqiang Yan, Hongyang Li, Kwang-Ting Cheng

Figure 1 for Exploring Intermediate Representation for Monocular Vehicle Pose Estimation
Figure 2 for Exploring Intermediate Representation for Monocular Vehicle Pose Estimation
Figure 3 for Exploring Intermediate Representation for Monocular Vehicle Pose Estimation
Figure 4 for Exploring Intermediate Representation for Monocular Vehicle Pose Estimation

  Access Paper or Ask Questions

ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse Coding


Oct 13, 2020
Yibo Yang, Hongyang Li, Shan You, Fei Wang, Chen Qian, Zhouchen Lin

Figure 1 for ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse Coding
Figure 2 for ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse Coding
Figure 3 for ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse Coding
Figure 4 for ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse Coding
* NeurIPS 2020 
  Access Paper or Ask Questions

Point-Set Anchors for Object Detection, Instance Segmentation and Pose Estimation


Jul 14, 2020
Fangyun Wei, Xiao Sun, Hongyang Li, Jingdong Wang, Stephen Lin

Figure 1 for Point-Set Anchors for Object Detection, Instance Segmentation and Pose Estimation
Figure 2 for Point-Set Anchors for Object Detection, Instance Segmentation and Pose Estimation
Figure 3 for Point-Set Anchors for Object Detection, Instance Segmentation and Pose Estimation
Figure 4 for Point-Set Anchors for Object Detection, Instance Segmentation and Pose Estimation
* To appear in ECCV 2020 
  Access Paper or Ask Questions

Dynamical System Inspired Adaptive Time Stepping Controller for Residual Network Families


Nov 23, 2019
Yibo Yang, Jianlong Wu, Hongyang Li, Xia Li, Tiancheng Shen, Zhouchen Lin

Figure 1 for Dynamical System Inspired Adaptive Time Stepping Controller for Residual Network Families
Figure 2 for Dynamical System Inspired Adaptive Time Stepping Controller for Residual Network Families
Figure 3 for Dynamical System Inspired Adaptive Time Stepping Controller for Residual Network Families
Figure 4 for Dynamical System Inspired Adaptive Time Stepping Controller for Residual Network Families
* AAAI-20 
  Access Paper or Ask Questions

SOGNet: Scene Overlap Graph Network for Panoptic Segmentation


Nov 18, 2019
Yibo Yang, Hongyang Li, Xia Li, Qijie Zhao, Jianlong Wu, Zhouchen Lin

Figure 1 for SOGNet: Scene Overlap Graph Network for Panoptic Segmentation
Figure 2 for SOGNet: Scene Overlap Graph Network for Panoptic Segmentation
Figure 3 for SOGNet: Scene Overlap Graph Network for Panoptic Segmentation
Figure 4 for SOGNet: Scene Overlap Graph Network for Panoptic Segmentation
* To appear in AAAI 2020. Our method also won the Innovation Award in COCO 2019 challenge 
  Access Paper or Ask Questions

Finding Task-Relevant Features for Few-Shot Learning by Category Traversal


May 27, 2019
Hongyang Li, David Eigen, Samuel Dodge, Matthew Zeiler, Xiaogang Wang

Figure 1 for Finding Task-Relevant Features for Few-Shot Learning by Category Traversal
Figure 2 for Finding Task-Relevant Features for Few-Shot Learning by Category Traversal
Figure 3 for Finding Task-Relevant Features for Few-Shot Learning by Category Traversal
Figure 4 for Finding Task-Relevant Features for Few-Shot Learning by Category Traversal
* CVPR 2019 
  Access Paper or Ask Questions

Feature Intertwiner for Object Detection


Mar 28, 2019
Hongyang Li, Bo Dai, Shaoshuai Shi, Wanli Ouyang, Xiaogang Wang

Figure 1 for Feature Intertwiner for Object Detection
Figure 2 for Feature Intertwiner for Object Detection
Figure 3 for Feature Intertwiner for Object Detection
Figure 4 for Feature Intertwiner for Object Detection
* ICLR 2019 
  Access Paper or Ask Questions

Neural Network Encapsulation


Aug 11, 2018
Hongyang Li, Xiaoyang Guo, Bo Dai, Wanli Ouyang, Xiaogang Wang

Figure 1 for Neural Network Encapsulation
Figure 2 for Neural Network Encapsulation
Figure 3 for Neural Network Encapsulation
Figure 4 for Neural Network Encapsulation
* ECCV 2018 
  Access Paper or Ask Questions

Zoom Out-and-In Network with Map Attention Decision for Region Proposal and Object Detection


Jun 08, 2018
Hongyang Li, Yu Liu, Wanli Ouyang, Xiaogang Wang

Figure 1 for Zoom Out-and-In Network with Map Attention Decision for Region Proposal and Object Detection
Figure 2 for Zoom Out-and-In Network with Map Attention Decision for Region Proposal and Object Detection
Figure 3 for Zoom Out-and-In Network with Map Attention Decision for Region Proposal and Object Detection
Figure 4 for Zoom Out-and-In Network with Map Attention Decision for Region Proposal and Object Detection
* Accepted by IJCV 
  Access Paper or Ask Questions

Recurrent Scale Approximation for Object Detection in CNN


Feb 08, 2018
Yu Liu, Hongyang Li, Junjie Yan, Fangyin Wei, Xiaogang Wang, Xiaoou Tang

Figure 1 for Recurrent Scale Approximation for Object Detection in CNN
Figure 2 for Recurrent Scale Approximation for Object Detection in CNN
Figure 3 for Recurrent Scale Approximation for Object Detection in CNN
Figure 4 for Recurrent Scale Approximation for Object Detection in CNN
* Accepted in ICCV 2017 
  Access Paper or Ask Questions

Rethinking Feature Discrimination and Polymerization for Large-scale Recognition


Oct 29, 2017
Yu Liu, Hongyang Li, Xiaogang Wang

Figure 1 for Rethinking Feature Discrimination and Polymerization for Large-scale Recognition
Figure 2 for Rethinking Feature Discrimination and Polymerization for Large-scale Recognition
Figure 3 for Rethinking Feature Discrimination and Polymerization for Large-scale Recognition
Figure 4 for Rethinking Feature Discrimination and Polymerization for Large-scale Recognition

  Access Paper or Ask Questions

Learning Deep Features via Congenerous Cosine Loss for Person Recognition


Mar 31, 2017
Yu Liu, Hongyang Li, Xiaogang Wang

Figure 1 for Learning Deep Features via Congenerous Cosine Loss for Person Recognition
Figure 2 for Learning Deep Features via Congenerous Cosine Loss for Person Recognition
Figure 3 for Learning Deep Features via Congenerous Cosine Loss for Person Recognition
Figure 4 for Learning Deep Features via Congenerous Cosine Loss for Person Recognition
* Post-rebuttal update. Add some comparison results; correct some technical part; rewrite some sections to make it more readable; code link available 
  Access Paper or Ask Questions

Zoom Out-and-In Network with Recursive Training for Object Proposal


Feb 19, 2017
Hongyang Li, Yu Liu, Wanli Ouyang, Xiaogang Wang

Figure 1 for Zoom Out-and-In Network with Recursive Training for Object Proposal
Figure 2 for Zoom Out-and-In Network with Recursive Training for Object Proposal
Figure 3 for Zoom Out-and-In Network with Recursive Training for Object Proposal
Figure 4 for Zoom Out-and-In Network with Recursive Training for Object Proposal

  Access Paper or Ask Questions

Dual Deep Network for Visual Tracking


Dec 19, 2016
Zhizhen Chi, Hongyang Li, Huchuan Lu, Ming-Hsuan Yang

Figure 1 for Dual Deep Network for Visual Tracking
Figure 2 for Dual Deep Network for Visual Tracking
Figure 3 for Dual Deep Network for Visual Tracking
Figure 4 for Dual Deep Network for Visual Tracking

  Access Paper or Ask Questions

Multi-Bias Non-linear Activation in Deep Neural Networks


Apr 03, 2016
Hongyang Li, Wanli Ouyang, Xiaogang Wang

Figure 1 for Multi-Bias Non-linear Activation in Deep Neural Networks
Figure 2 for Multi-Bias Non-linear Activation in Deep Neural Networks
Figure 3 for Multi-Bias Non-linear Activation in Deep Neural Networks
Figure 4 for Multi-Bias Non-linear Activation in Deep Neural Networks

  Access Paper or Ask Questions

LCNN: Low-level Feature Embedded CNN for Salient Object Detection


Aug 17, 2015
Hongyang Li, Huchuan Lu, Zhe Lin, Xiaohui Shen, Brian Price

Figure 1 for LCNN: Low-level Feature Embedded CNN for Salient Object Detection
Figure 2 for LCNN: Low-level Feature Embedded CNN for Salient Object Detection
Figure 3 for LCNN: Low-level Feature Embedded CNN for Salient Object Detection
Figure 4 for LCNN: Low-level Feature Embedded CNN for Salient Object Detection

  Access Paper or Ask Questions

Inner and Inter Label Propagation: Salient Object Detection in the Wild


May 27, 2015
Hongyang Li, Huchuan Lu, Zhe Lin, Xiaohui Shen, Brian Price

Figure 1 for Inner and Inter Label Propagation: Salient Object Detection in the Wild
Figure 2 for Inner and Inter Label Propagation: Salient Object Detection in the Wild
Figure 3 for Inner and Inter Label Propagation: Salient Object Detection in the Wild
Figure 4 for Inner and Inter Label Propagation: Salient Object Detection in the Wild
* The full version of the TIP 2015 publication 
  Access Paper or Ask Questions