Zilong Zhong

Computer Vision and Pattern Recognition · 2018–2020

A researcher in the Science corpus: 3 papers; trusts 390 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 125 citations into it
• Computational Mechanics — 44 citations into it
• Artificial Intelligence — 39 citations into it
• Aerospace Engineering — 28 citations into it
• Environmental Engineering — 27 citations into it
• Model Architectures — 20 citations into it
• Media Technology — 18 citations into it; #2,205 of 3,621 by trust here, #1,498 by citations
• 3D Vision & Pose — 16 citations into it

Whom Zilong Zhong trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Ross B. Girshick
5. Karen Simonyan
6. X. Zhang
7. Yann LeCun
8. Yoshua Bengio
9. Ilya Sutskever
10. Christian Szegedy

Who trusts Zilong Zhong overall

1. Swalpa Kumar Roy
2. Mercedes Eugenia Paoletti
3. Guangrui Zhao
4. Xin He
5. Ava Vali
6. Qichao Liu
7. Alberto Signoroni
8. Wenju Wang
9. Juan Mario Haut
10. Rui Li

Papers from Zilong Zhong's perspective

Ordered by how strongly the researchers Zilong Zhong trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Attention is All you Need (Neural Information Processing Systems)
8. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Squeeze-and-Attention Networks for Semantic Segmentation (2019) — 287 citations
• Spectral–Spatial Residual Network for Hyperspectral Image Classification: A 3-D Deep Learning Framework (2018) — 1,589 citations
• Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review (2020) — 547 citations