Zan Gojcic

Computer Vision and Pattern Recognition · 2018–2022

A researcher in the Science corpus: 4 papers; trusts 350 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 155 citations into it
• Computational Mechanics — 79 citations into it
• Generative Models — 72 citations into it
• Computer Graphics and Computer-Aided Design — 53 citations into it
• Aerospace Engineering — 48 citations into it; #2,013 of 6,421 by trust here, #3,280 by citations
• Artificial Intelligence — 44 citations into it
• Geology — 36 citations into it; #351 of 687 by trust here, #474 by citations
• 3D Vision & Pose — 32 citations into it

Whom Zan Gojcic trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Alex Krizhevsky
4. Yoshua Bengio
5. Kaiming He
6. Radu Bogdan Rusu
7. Sergey Ioffe
8. Yann LeCun
9. Ilya Sutskever
10. Charles Ruizhongtai Qi

Who trusts Zan Gojcic overall

1. Xiaoshui Huang
2. Christopher Bongsoo Choy
3. Xuyang Bai
4. Fu Li
5. Yue Wang
6. Guofeng Mei
7. Heng Yang
8. Zixin Luo
9. Shengyu Huang
10. Sheng Ao

Papers from Zan Gojcic's perspective

Ordered by how strongly the researchers Zan Gojcic 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. ImageNet: A large-scale hierarchical image database
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• LION: Latent Point Diffusion Models for 3D Shape Generation (2022) — 720 citations
• GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images (2022) — 636 citations
• PREDATOR: Registration of 3D Point Clouds with Low Overlap (2020) — 728 citations
• The Perfect Match: 3D Point Cloud Matching With Smoothed Densities (2018) — 543 citations