Joseph Redmon

Computer Vision and Pattern Recognition · 2005–2018

A researcher in the Science corpus: 8 papers; trusts 941 researchers and is trusted by 8,158 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 191 citations into it; #239 of 21,575 by trust here, #178 by citations
• Artificial Intelligence — 117 citations into it; #2,561 of 35,345 by trust here, #2,194 by citations
• Reinforcement Learning — 34 citations into it
• Aerospace Engineering — 26 citations into it
• Model Architectures — 25 citations into it
• Evaluation & Benchmarks — 22 citations into it
• Control and Systems Engineering — 20 citations into it; #564 of 8,257 by trust here, #1,875 by citations
• Efficiency & Compression — 18 citations into it; #66 of 1,264 by trust here, #81 by citations

Whom Joseph Redmon trusts overall

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

1. Ross B. Girshick
2. Kaiming He
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Christian Szegedy
6. Shaoqing Ren
7. Ilya Sutskever
8. David G. Lowe
9. Karen Simonyan
10. Yoshua Bengio

Who trusts Joseph Redmon overall

1. Chien-Yao Wang
2. Zeming Li
3. Zhaohui Zheng
4. Chunhua Shen
5. Xingyi Zhou
6. Bo Li
7. Xiaogang Wang
8. Songtao Liu
9. Alexey Bochkovskiy
10. Daniel Bolya

Papers from Joseph Redmon's perspective

Ordered by how strongly the researchers Joseph Redmon 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
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. Going deeper with convolutions

Their papers

• You Only Look Once: Unified, Real-Time Object Detection (2015) — 46,861 citations
• YOLO9000: Better, Faster, Stronger (2016) — 17,782 citations
• XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks (2016) — 4,754 citations
• YOLOv3: An Incremental Improvement (2018) — 25,668 citations
• IQA: Visual Question Answering in Interactive Environments (2017) — 435 citations
• Real-time grasp detection using convolutional neural networks (2014) — 874 citations
• Who Let the Dogs Out? Modeling Dog Behavior from Visual Data (2018) — 63 citations
• Decrease in Anogenital Distance among Male Infants with Prenatal Phthalate Exposure (2005) — 1,596 citations