Atsushi Kanehira

Computer Vision and Pattern Recognition · 2018

A researcher in the Science corpus: 3 papers; trusts 504 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 63 citations into it; #20,711 of 21,575 by trust here, #17,258 by citations
• Artificial Intelligence — 35 citations into it
• Safety, Robustness & Interpretability — 15 citations into it; #1,453 of 2,251 by trust here, #2,110 by citations
• Model Architectures — 7 citations into it
• Multimodal Learning — 7 citations into it
• Evaluation & Benchmarks — 5 citations into it

Whom Atsushi Kanehira trusts overall

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

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

Who trusts Atsushi Kanehira overall

1. D. V. Carvalho
2. Matthew R. O'Shaughnessy
3. Ilia Stepin
4. Raha Moraffah
5. Octavio Loyola-González
6. Eduardo Marques Pereira
7. Tatsuya Harada
8. Kentaro Takemoto
9. Gregory H. Canal
10. Jaime S. Cardoso

Papers from Atsushi Kanehira's perspective

Ordered by how strongly the researchers Atsushi Kanehira 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. 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. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Viewpoint-Aware Video Summarization (2018) — 39 citations
• Multimodal Explanations by Predicting Counterfactuality in Videos (2018) — 43 citations
• Learning to Explain With Complemental Examples (2018) — 44 citations