Weiyue Wang

Computer Vision and Pattern Recognition · with Neumann · 2017–2019

A researcher in the Science corpus: 5 papers; trusts 869 researchers and is trusted by 39 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 128 citations into it; #6,230 of 21,575 by trust here, #4,750 by citations
• Computational Mechanics — 59 citations into it; #2,114 of 10,001 by trust here, #1,190 by citations
• 3D Vision & Pose — 42 citations into it; #504 of 689 by trust here, #220 by citations
• Aerospace Engineering — 41 citations into it
• Artificial Intelligence — 33 citations into it
• Geology — 26 citations into it; #90 of 687 by trust here, #72 by citations
• Generative Models — 21 citations into it
• Computer Graphics and Computer-Aided Design — 18 citations into it; #702 of 1,584 by trust here, #486 by citations

Whom Weiyue Wang trusts overall

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

1. Kaiming He
2. Diederik P. Kingma
3. Ross B. Girshick
4. Alex Krizhevsky
5. Geoffrey E. Hinton
6. Ilya Sutskever
7. Yann LeCun
8. Yoshua Bengio
9. Karen Simonyan
10. Shuran Song

Who trusts Weiyue Wang overall

1. H. Wang
2. Zhijian Liu
3. Shaoshuai Shi
4. Christopher Xie
5. Hengli Wang
6. Yangyan Li
7. Bo Yang
8. Ji Hou
9. Lin-Zhuo Chen
10. Huayao Liu

Papers from Weiyue Wang's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
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

• DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction (2019) — 619 citations
• SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation (2017) — 616 citations
• Shape Inpainting Using 3D Generative Adversarial Network and Recurrent Convolutional Networks (2017) — 186 citations
• Depth-aware CNN for RGB-D Segmentation (2018) — 292 citations
• Recurrent Slice Networks for 3D Segmentation of Point Clouds (2018) — 484 citations