Ben Upcroft

Computer Vision and Pattern Recognition · 2012–2018

A researcher in the Science corpus: 14 papers; trusts 737 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 251 citations into it; #5,790 of 21,575 by trust here, #3,124 by citations
• Artificial Intelligence — 134 citations into it; #22,356 of 35,345 by trust here, #10,798 by citations
• Aerospace Engineering — 86 citations into it; #2,293 of 6,421 by trust here, #1,520 by citations
• Representation & Self-Supervised Learning — 39 citations into it
• Reinforcement Learning — 31 citations into it
• Evaluation & Benchmarks — 28 citations into it
• Media Technology — 19 citations into it
• Model Architectures — 15 citations into it

Whom Ben Upcroft trusts overall

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

1. Geoffrey E. Hinton
2. Antonio Torralba
3. Alex Krizhevsky
4. Andreas Geiger
5. Ross B. Girshick
6. David G. Lowe
7. Kaiming He
8. Yann LeCun
9. Yoshua Bengio
10. Ilya Sutskever

Who trusts Ben Upcroft overall

1. Lei Tai
2. Niko Sünderhauf
3. Richard Zhang

Papers from Ben Upcroft's perspective

Ordered by how strongly the researchers Ben Upcroft 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Simple online and realtime tracking (2016) — 4,063 citations
• Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control (2015) — 289 citations
• On the performance of ConvNet features for place recognition (2015) — 555 citations
• Place Recognition with ConvNet Landmarks: Viewpoint-Robust, Condition-Robust, Training-Free (2015) — 409 citations
• Nonparametric semantic segmentation for 3D street scenes (2013) — 46 citations
• The limits and potentials of deep learning for robotics (2018) — 566 citations
• Place categorization and semantic mapping on a mobile robot (2015) — 144 citations
• DeepFruits: A Fruit Detection System Using Deep Neural Networks (2016) — 1,000 citations
• High-fidelity simulation for evaluating robotic vision performance (2016) — 22 citations
• Vision-Based Reaching Using Modular Deep Networks: from Simulation to the Real World (2016) — 18 citations
• A Bayesian approach for place recognition (2012) — 21 citations
• Deep learning features at scale for visual place recognition (2017) — 359 citations
• The ACRV picking benchmark: A robotic shelf picking benchmark to foster reproducible research (2016) — 83 citations
• Illumination Invariant Imaging : Applications in Robust Vision-based Localisation , Mapping and Classification for Autonomous Vehicles (2014) — 146 citations