Toshihiko Yamasaki

Computer Vision and Pattern Recognition · with Aizawa · 2015–2022

A researcher in the Science corpus: 6 papers; trusts 897 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 138 citations into it; #6,572 of 21,575 by trust here, #4,317 by citations
• Artificial Intelligence — 63 citations into it; #10,782 of 35,345 by trust here, #8,157 by citations
• Representation & Self-Supervised Learning — 20 citations into it
• Optimization & Training — 13 citations into it
• Evaluation & Benchmarks — 11 citations into it
• Information Retrieval & Search — 11 citations into it
• Generative Models — 10 citations into it
• Model Architectures — 9 citations into it

Whom Toshihiko Yamasaki trusts overall

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

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

Who trusts Toshihiko Yamasaki overall

1. S. Gadre
2. Junnan Li
3. Ping Hu
4. Chuangchuang Tan
5. Zhendong Wang
6. Mitchell Wortsman
7. Conghui Hu
8. Pengfei Chen
9. Garrett Wilson
10. Görkem Algan

Papers from Toshihiko Yamasaki's perspective

Ordered by how strongly the researchers Toshihiko Yamasaki 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
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

• 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
• Sketch-based manga retrieval using manga109 dataset (2015) — 1,476 citations
• Detecting Deepfakes with Self-Blended Images (2022) — 588 citations
• Manga109 dataset and creation of metadata (2016) — 167 citations