Hiroharu Kato

Computer Vision and Pattern Recognition · 2014–2019

A researcher in the Science corpus: 3 papers; trusts 890 researchers and is trusted by 943 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 107 citations into it; #1,414 of 21,575 by trust here, #5,843 by citations
• Computational Mechanics — 34 citations into it; #742 of 10,001 by trust here, #1,325 by citations
• 3D Vision & Pose — 31 citations into it
• Computer Graphics and Computer-Aided Design — 21 citations into it; #137 of 1,584 by trust here, #309 by citations
• Aerospace Engineering — 20 citations into it
• Generative Models — 20 citations into it; #319 of 1,956 by trust here, #717 by citations
• Artificial Intelligence — 16 citations into it
• Geology — 11 citations into it

Whom Hiroharu Kato trusts overall

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

1. Alex Krizhevsky
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Kaiming He
5. David G. Lowe
6. Yann LeCun
7. Alex Graves
8. Ilya Sutskever
9. Yoshua Bengio
10. Karen Simonyan

Who trusts Hiroharu Kato overall

1. Aravindh Mahendran
2. Andrea Vedaldi
3. Shichen Liu
4. Alexey Dosovitskiy
5. Jitendra Malik
6. Vincent Sitzmann
7. Angjoo Kanazawa
8. Yana Hasson
9. Georgia Gkioxari
10. Thomas Brox

Papers from Hiroharu Kato's perspective

Ordered by how strongly the researchers Hiroharu Kato 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

• Neural 3D Mesh Renderer (2017) — 1,168 citations
• Image Reconstruction from Bag-of-Visual-Words (2014) — 86 citations
• Self-supervised Learning of 3D Objects from Natural Images (2019) — 7 citations