Kiyoharu Aizawa

Computer Vision and Pattern Recognition · 2014–2019

A researcher in the Science corpus: 8 papers; trusts 1,019 researchers and is trusted by 32 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 149 citations into it; #5,301 of 21,575 by trust here, #3,875 by citations
• Artificial Intelligence — 83 citations into it; #8,192 of 35,345 by trust here, #7,531 by citations
• Representation & Self-Supervised Learning — 30 citations into it
• Evaluation & Benchmarks — 14 citations into it
• Model Architectures — 14 citations into it
• Information Retrieval & Search — 13 citations into it
• Optimization & Training — 12 citations into it
• Radiology, Nuclear Medicine and Imaging — 12 citations into it

Whom Kiyoharu Aizawa trusts overall

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

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

Who trusts Kiyoharu Aizawa overall

1. Jingkang Yang
2. Haoqi Wang
3. Keiji Yanai
4. S. Gadre
5. Mohammad Rostami
6. Saikiran Bulusu
7. Yu Liu
8. Junnan Li
9. Keke Tang
10. Weiqing Min

Papers from Kiyoharu Aizawa's perspective

Ordered by how strongly the researchers Kiyoharu Aizawa 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

• Cross-Domain Weakly-Supervised Object Detection Through Progressive Domain Adaptation (2018) — 629 citations
• Joint Optimization Framework for Learning with Noisy Labels (2018) — 806 citations
• Zero-Shot Semantic Segmentation via Variational Mapping (2019) — 55 citations
• Unsupervised Out-of-Distribution Detection by Maximum Classifier Discrepancy (2019) — 200 citations
• Sketch-based manga retrieval using manga109 dataset (2015) — 1,476 citations
• Significance of Softmax-Based Features in Comparison to Distance Metric Learning-Based Features (2017) — 70 citations
• Food Detection and Recognition Using Convolutional Neural Network (2014) — 295 citations
• Manga109 dataset and creation of metadata (2016) — 167 citations