Zhizhong Han

Computer Vision and Pattern Recognition · 2018–2021

A researcher in the Science corpus: 5 papers; trusts 254 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 87 citations into it; #14,338 of 21,575 by trust here, #10,154 by citations
• Computational Mechanics — 69 citations into it; #6,445 of 10,001 by trust here, #3,190 by citations
• Geology — 36 citations into it; #454 of 687 by trust here, #324 by citations
• 3D Vision & Pose — 23 citations into it
• Aerospace Engineering — 22 citations into it
• Artificial Intelligence — 22 citations into it
• Model Architectures — 18 citations into it
• Generative Models — 14 citations into it

Whom Zhizhong Han trusts overall

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

1. Geoffrey E. Hinton
2. Karen Simonyan
3. Charles Ruizhongtai Qi
4. Alex Krizhevsky
5. Kaiming He
6. Yoshua Bengio
7. Diederik P. Kingma
8. Yann LeCun
9. Ilya Sutskever
10. Andrew Zisserman

Who trusts Zhizhong Han overall

1. Jonathan Sauder
2. Yongming Rao
3. Mikaela Angelina Uy
4. Hanchen Wang
5. Peng Xiang
6. Bjarne Sievers
7. Zhiyuan Zhang
8. Binh-Son Hua
9. Qi Liu
10. Jiwen Lu

Papers from Zhizhong Han's perspective

Ordered by how strongly the researchers Zhizhong Han 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
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

• Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Network (2018) — 359 citations
• SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer (2021) — 340 citations
• 3D2SeqViews: Aggregating Sequential Views for 3D Global Feature Learning by CNN With Hierarchical Attention Aggregation (2019) — 180 citations
• Y^2Seq2Seq: Cross-Modal Representation Learning for 3D Shape and Text by Joint Reconstruction and Prediction of View and Word Sequences (2018) — 62 citations
• View Inter-Prediction GAN: Unsupervised Representation Learning for 3D Shapes by Learning Global Shape Memories to Support Local View Predictions (2018) — 143 citations