John Lambert

Computer Vision and Pattern Recognition · 2019–2023

A researcher in the Science corpus: 4 papers; trusts 617 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 157 citations into it; #18,043 of 21,575 by trust here, #5,843 by citations
• Artificial Intelligence — 59 citations into it; #32,915 of 35,345 by trust here, #16,166 by citations
• Evaluation & Benchmarks — 58 citations into it; #2,828 of 3,147 by trust here, #2,545 by citations
• Model Architectures — 14 citations into it
• Representation & Self-Supervised Learning — 11 citations into it
• Computational Mechanics — 8 citations into it
• Safety, Robustness & Interpretability — 8 citations into it

Whom John Lambert trusts overall

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

1. Kaiming He
2. Ross B. Girshick
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Antonio Torralba
6. Piotr Doll'ar
7. Diederik P. Kingma
8. Yann LeCun
9. Yoshua Bengio
10. Ilya Sutskever

Who trusts John Lambert overall

1. Golnaz Ghiasi
2. Vladlen Koltun
3. Xingyi Zhou
4. Stephan R. Richter
5. Xiuye Gu
6. Boyi Li
7. Qiang Zhou
8. Yicheng Liu
9. Songyou Peng
10. Philipp Krahenbuhl

Papers from John Lambert's perspective

Ordered by how strongly the researchers John Lambert 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. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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

• Argoverse: 3D Tracking and Forecasting With Rich Maps (2019) — 1,640 citations
• MSeg: A Composite Dataset for Multi-Domain Semantic Segmentation (2020) — 241 citations
• Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting (2023) — 1,097 citations
• MSeg: A Composite Dataset for Multi-Domain Semantic Segmentation (2022) — 13 citations