Arjun Jain

Computer Vision and Pattern Recognition · 2011–2019

A researcher in the Science corpus: 9 papers; trusts 783 researchers and is trusted by 3,574 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 220 citations into it; #566 of 21,575 by trust here, #1,284 by citations
• Artificial Intelligence — 51 citations into it
• Human-Computer Interaction — 36 citations into it; #43 of 2,253 by trust here, #34 by citations
• 3D Vision & Pose — 23 citations into it
• Biomedical Engineering — 21 citations into it; #166 of 10,734 by trust here, #825 by citations
• Optimization & Training — 17 citations into it
• Evaluation & Benchmarks — 14 citations into it
• ML Systems & Hardware — 14 citations into it

Whom Arjun Jain trusts overall

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

1. Geoffrey E. Hinton
2. Navneet Dalal
3. Yann LeCun
4. Alex Krizhevsky
5. David G. Lowe
6. Jitendra Malik
7. Ilya Sutskever
8. Pedro F. Felzenszwalb
9. Yoshua Bengio
10. Kaiming He

Who trusts Arjun Jain overall

1. Juergen Gall
2. Andrew Zisserman
3. Tomas Pfister
4. Wanli Ouyang
5. Du Tran
6. Jonathan Tompson
7. Yanming Guo
8. Dushyant Mehta
9. Xiaogang Wang
10. Xiao Chu

Papers from Arjun Jain's perspective

Ordered by how strongly the researchers Arjun Jain 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. 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

• Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation (2014) — 1,614 citations
• Efficient object localization using Convolutional Networks (2014) — 1,451 citations
• Articulated people detection and pose estimation: Reshaping the future (2012) — 258 citations
• Learning Human Pose Estimation Features with Convolutional Networks (2013) — 218 citations
• MoDeep: A Deep Learning Framework Using Motion Features for Human Pose Estimation (2014) — 176 citations
• Efficient ConvNet-based marker-less motion capture in general scenes with a low number of cameras (2015) — 162 citations
• Learning people detection models from few training samples (2011) — 92 citations
• ProtoGAN: Towards Few Shot Learning for Action Recognition (2019) — 108 citations
• Theano: A Python framework for fast computation of mathematical expressions (2016) — 2,380 citations