Ziyu Zhang

Computer Vision and Pattern Recognition · 2015–2018

A researcher in the Science corpus: 6 papers; trusts 1,146 researchers and is trusted by 596 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 187 citations into it; #1,751 of 21,575 by trust here, #2,902 by citations
• Artificial Intelligence — 88 citations into it; #18,555 of 35,345 by trust here, #25,809 by citations
• Multimodal Learning — 32 citations into it
• Aerospace Engineering — 26 citations into it; #2,444 of 6,421 by trust here, #988 by citations
• Generative Models — 25 citations into it
• Model Architectures — 23 citations into it
• Natural Language Processing — 19 citations into it; #5,086 of 5,688 by trust here, #3,826 by citations
• Probabilistic & Bayesian Methods — 14 citations into it

Whom Ziyu Zhang trusts overall

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

1. Ross B. Girshick
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Karen Simonyan
5. Kaiming He
6. Raquel Urtasun
7. Yann LeCun
8. Andrew Zisserman
9. Diederik P. Kingma
10. Yoshua Bengio

Who trusts Ziyu Zhang overall

1. Jonas Uhrig
2. Shu Liu
3. Raquel Urtasun
4. Sanja Fidler
5. Mengye Ren
6. Min Bai
7. Xiaozhi Chen
8. Liang-Chieh Chen
9. Xiaodan Liang
10. Evgeny Levinkov

Papers from Ziyu Zhang's perspective

Ordered by how strongly the researchers Ziyu Zhang 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. Long Short-Term Memory
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Monocular Object Instance Segmentation and Depth Ordering with CNNs (2015) — 164 citations
• Monocular 3D Object Detection for Autonomous Driving (2016) — 1,014 citations
• Instance-Level Segmentation for Autonomous Driving with Deep Densely Connected MRFs (2015) — 222 citations
• Instance-Level Segmentation with Deep Densely Connected MRFs (2015) — 29 citations
• Creativity: Generating Diverse Questions Using Variational Autoencoders (2017) — 159 citations
• Generative Modeling Using the Sliced Wasserstein Distance (2018) — 260 citations