Samuel Schulter

Computer Vision and Pattern Recognition · 2013–2022

A researcher in the Science corpus: 9 papers; trusts 989 researchers and is trusted by 319 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 208 citations into it; #2,258 of 21,575 by trust here, #2,783 by citations
• Artificial Intelligence — 123 citations into it; #8,001 of 35,345 by trust here, #4,797 by citations
• Representation & Self-Supervised Learning — 40 citations into it
• Evaluation & Benchmarks — 24 citations into it
• Media Technology — 24 citations into it; #392 of 3,621 by trust here, #693 by citations
• Model Architectures — 17 citations into it
• Generative Models — 16 citations into it; #1,614 of 1,956 by trust here, #1,681 by citations
• Optimization & Training — 12 citations into it

Whom Samuel Schulter trusts overall

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

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

Who trusts Samuel Schulter overall

1. Chao Dong
2. Xiao-Jiao Mao
3. Luc Van Gool
4. Jiwon Kim
5. Radu Timofte
6. Wei-Sheng Lai
7. Kai Zhang
8. Chen Change Loy
9. Jordi Salvador
10. Eduardo Pérez-Pellitero

Papers from Samuel Schulter's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Attention is All you Need (Neural Information Processing Systems)
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Learning to Adapt Structured Output Space for Semantic Segmentation (2018) — 1,735 citations
• Fast and accurate image upscaling with super-resolution forests (2015) — 595 citations
• Deep Network Flow for Multi-object Tracking (2017) — 213 citations
• Alternating Decision Forests (2013) — 73 citations
• Domain Adaptation for Structured Output via Discriminative Patch Representations (2019) — 334 citations
• Conditioned Regression Models for Non-blind Single Image Super-Resolution (2015) — 103 citations
• Alternating Regression Forests for Object Detection and Pose Estimation (2013) — 53 citations
• On Generalizing Beyond Domains in Cross-Domain Continual Learning (2022) — 44 citations
• Learning To Simulate (2018) — 123 citations