Or Litany

Computer Vision and Pattern Recognition · 2017–2022

A researcher in the Science corpus: 17 papers; trusts 1,409 researchers and is trusted by 93 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 621 citations into it; #3,361 of 21,575 by trust here, #2,983 by citations
• Computational Mechanics — 348 citations into it; #2,198 of 10,001 by trust here, #457 by citations
• Artificial Intelligence — 228 citations into it
• Computer Graphics and Computer-Aided Design — 175 citations into it; #1,160 of 1,584 by trust here, #257 by citations
• Aerospace Engineering — 144 citations into it
• Geology — 131 citations into it; #143 of 687 by trust here, #144 by citations
• 3D Vision & Pose — 119 citations into it; #621 of 689 by trust here, #485 by citations
• Generative Models — 107 citations into it

Whom Or Litany trusts overall

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

1. Diederik P. Kingma
2. Kaiming He
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Charles Ruizhongtai Qi
6. Yann LeCun
7. Ross B. Girshick
8. Yoshua Bengio
9. Ilya Sutskever
10. Jimmy Ba

Who trusts Or Litany overall

1. Yue Wang
2. Matan Atzmon
3. Haggai Maron
4. Federico Monti
5. Justin M. Solomon
6. Tianwei Yin
7. Xian-Feng Han
8. Nikos Kolotouros
9. Chaitanya Patel
10. Rana Hanocka

Papers from Or Litany's perspective

Ordered by how strongly the researchers Or Litany 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Deep Hough Voting for 3D Object Detection in Point Clouds (2019) — 1,503 citations
• ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation (2020) — 87 citations
• LION: Latent Point Diffusion Models for 3D Shape Generation (2022) — 720 citations
• Deformable Shape Completion with Graph Convolutional Autoencoders (2017) — 241 citations
• PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding (2020) — 838 citations
• ImVoteNet: Boosting 3D Object Detection in Point Clouds With Image Votes (2020) — 300 citations
• ReLMoGen: Integrating Motion Generation in Reinforcement Learning for Mobile Manipulation (2021) — 80 citations
• Neural Fields in Visual Computing and Beyond (2021) — 820 citations
• Language-Grounded Indoor 3D Semantic Segmentation in the Wild (2022) — 313 citations
• Deep Functional Maps: Structured Prediction for Dense Shape Correspondence (2017) — 346 citations
• On Learning Sets of Symmetric Elements (2020) — 152 citations
• Mask3D: Mask Transformer for 3D Semantic Instance Segmentation (2022) — 409 citations
• Learning Smooth Neural Functions via Lipschitz Regularization (2022) — 135 citations
• GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images (2022) — 636 citations
• Vector Neurons: A General Framework for SO(3)-Equivariant Networks (2021) — 435 citations
• Dual-Primal Graph Convolutional Networks (2018) — 84 citations
• Self-supervised Learning of Dense Shape Correspondence (2018) — 23 citations