Vladimir I. Iglovikov

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 16 papers; trusts 713 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 163 citations into it; #4,906 of 21,575 by trust here, #4,555 by citations
• Artificial Intelligence — 115 citations into it; #11,013 of 35,345 by trust here, #13,544 by citations
• Model Architectures — 53 citations into it
• Media Technology — 40 citations into it; #1,190 of 3,621 by trust here, #1,365 by citations
• Optimization & Training — 39 citations into it
• Biophysics — 29 citations into it
• Evaluation & Benchmarks — 23 citations into it
• Ocean Engineering — 12 citations into it

Whom Vladimir I. Iglovikov trusts overall

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

1. Kaiming He
2. Karen Simonyan
3. Diederik P. Kingma
4. Olaf Ronneberger
5. Evan Shelhamer
6. Andrew Zisserman
7. Sergey Ioffe
8. Geoffrey E. Hinton
9. X. Zhang
10. Alex Krizhevsky

Who trusts Vladimir I. Iglovikov overall

1. Alexey A. Shvets
2. Alexander Rakhlin
3. Alexander V. Buslaev
4. Alexandr A Kalinin
5. Selim S. Seferbekov
6. Yauhen Babakhin
7. Aleksei Tiulpin
8. Artur Kuzin
9. S. Hasan
10. Onur Tasar

Papers from Vladimir I. Iglovikov's perspective

Ordered by how strongly the researchers Vladimir I. Iglovikov 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. Attention is All you Need (Neural Information Processing Systems)
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• One Thousand and One Hours: Self-driving Motion Prediction Dataset (2020) — 483 citations
• Albumentations: fast and flexible image augmentations (2018) — 2,644 citations
• Automatic Instrument Segmentation in Robot-Assisted Surgery Using Deep Learning (2018) — 388 citations
• 2017 Robotic Instrument Segmentation Challenge (2019) — 310 citations
• Medical Image Segmentation Using Deep Neural Networks with Pre-trained Encoders (2020) — 47 citations
• Breast Tumor Cellularity Assessment using Deep Neural Networks (2019) — 24 citations
• TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation (2018) — 677 citations
• Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis (2018) — 343 citations
• Angiodysplasia Detection and Localization Using Deep Convolutional Neural Networks (2018) — 75 citations
• Fully Convolutional Network for Automatic Road Extraction from Satellite Imagery (2018) — 164 citations
• Camera Model Identification Using Convolutional Neural Networks (2018) — 28 citations
• Convolutional neural network application for cells segmentation in immunocytochemical study (2018) — 7 citations
• Feature Pyramid Network for Multi-class Land Segmentation (2018) — 165 citations
• TernausNetV2: Fully Convolutional Network for Instance Segmentation (2018) — 160 citations
• Satellite Imagery Feature Detection using Deep Convolutional Neural Network: A Kaggle Competition (2017) — 178 citations
• Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks (2017) — 128 citations