Zhang, Xiangyu

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 235,474

Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV)

Advanced Neural Network ApplicationsNeural Networks and Applications

Cited by 19,741

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence

Advanced Neural Network ApplicationsAdvanced Image and Video Retrieval Techniques

Cited by 11,971

Identity mappings in deep residual networks

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · Lecture Notes in Computer Science

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 10,741

Identity Mappings in Deep Residual Networks

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 10,665

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

Zhang, X., Zhou, X., Lin, M., & Sun, J. (2017) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 9,982

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

Zhang, X., Zhou, X., Lin, M., & Sun, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 9,982

ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design

Ma, N., Zhang, X., Zheng, H.-T., & Sun, J. (2018) · Lecture notes in computer science

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 7,356

ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design

Ma, N., Zhang, X., Zheng, H.-T., & Sun, J. (2018) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 7,344

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2015) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 4,196

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2014) · Lecture Notes in Computer Science

Advanced Neural Network ApplicationsAdvanced Image and Video Retrieval Techniques

Cited by 3,034

RepVGG: Making VGG-style ConvNets Great Again

Ding, X., Zhang, X., Ma, N., Han, J., Ding, G., & Sun, J. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 2,907

Channel Pruning for Accelerating Very Deep Neural Networks

He, Y., Zhang, X., & Sun, J. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV)

Advanced Neural Network ApplicationsMachine Learning and Data Classification

Cited by 2,647

Large Kernel Matters — Improve Semantic Segmentation by Global Convolutional Network

Peng, C., Zhang, X., Yu, G., Luo, G., & Sun, J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 1,750

MOTR: End-to-End Multiple-Object Tracking with Transformer

Zeng, F., Dong, B., Zhang, Y., Wang, T., Zhang, X., & Wei, Y. (2022) · Lecture Notes in Computer Science

Video Surveillance and Tracking MethodsAdvanced Neural Network Applications

Cited by 752

Objects365: A Large-Scale, High-Quality Dataset for Object Detection

Shao, S., Li, Z., Zhang, T., Peng, C., Yu, G., Zhang, X., Li, J., & Sun, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV)

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 673

Bounding Box Regression With Uncertainty for Accurate Object Detection

He, Y., Zhu, C., Wang, J., Savvides, M., & Zhang, X. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsVisual Attention and Saliency Detection

Cited by 623

CrowdHuman: A Benchmark for Detecting Human in a Crowd

Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., & Sun, J. (2018) · arXiv

Advanced Neural Network ApplicationsHuman Pose and Action Recognition

Cited by 534

VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking

Chen, Y., Liu, J., Zhang, X., Qi, X., & Jia, J. (2023) · arXiv

Advanced Neural Network ApplicationsAutonomous Vehicle Technology and Safety

Cited by 486

Object Detection Networks on Convolutional Feature Maps

Ren, S., He, K., Girshick, R., Zhang, X., & Sun, J. (2015) · arXiv

Advanced Neural Network ApplicationsAdvanced Image and Video Retrieval Techniques

Cited by 454

Object Detection Networks on Convolutional Feature Maps

Ren, S., He, K., Girshick, R., Zhang, X., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence

Advanced Neural Network ApplicationsAdvanced Image and Video Retrieval Techniques

Cited by 454

MegDet: A Large Mini-Batch Object Detector

Peng, C., Xiao, T., Li, Z., Jiang, Y., Zhang, X., Jia, K., Yu, G., & Sun, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 311

Exploring Object-Centric Temporal Modeling for Efficient Multi-View 3D Object Detection

Wang, S., Liu, Y., Wang, T., Li, Y., & Zhang, X. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Advanced Neural Network ApplicationsAutonomous Vehicle Technology and Safety

Cited by 292

Light-Head R-CNN: In Defense of Two-Stage Object Detector

Li, Z., Peng, C., Yu, G., Zhang, X., Deng, Y., & Sun, J. (2017) · arXiv

Advanced Neural Network ApplicationsVisual Attention and Saliency Detection

Cited by 292