Adrian Bulat

Computer Vision and Pattern Recognition · 2016–2020

A researcher in the Science corpus: 9 papers; trusts 1,306 researchers and is trusted by 507 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 239 citations into it; #1,881 of 21,575 by trust here, #3,633 by citations
• Artificial Intelligence — 84 citations into it
• Model Architectures — 38 citations into it
• Efficiency & Compression — 25 citations into it
• Generative Models — 24 citations into it
• Media Technology — 24 citations into it
• Optimization & Training — 24 citations into it
• Biomedical Engineering — 20 citations into it; #569 of 10,734 by trust here, #3,551 by citations

Whom Adrian Bulat trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Ross B. Girshick
5. Karen Simonyan
6. X. Zhang
7. Diederik P. Kingma
8. Yann LeCun
9. Andrew Zisserman
10. Christian Szegedy

Who trusts Adrian Bulat overall

1. Aliaksandr Siarohin
2. Xuanyi Dong
3. Jian Zhao
4. Zhihao Wang
5. Georgios Tzimiropoulos
6. Nantheera Anantrasirichai
7. Timo Bolkart
8. Hang Zhou
9. Michael J. Black
10. Soubhik Sanyal

Papers from Adrian Bulat's perspective

Ordered by how strongly the researchers Adrian Bulat 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. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
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

• Human Pose Estimation via Convolutional Part Heatmap Regression (2016) — 549 citations
• How Far are We from Solving the 2D & 3D Face Alignment Problem? (and a Dataset of 230,000 3D Facial Landmarks) (2017) — 1,678 citations
• Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression (2017) — 471 citations
• To learn image super-resolution, use a GAN to learn how to do image degradation first (2018) — 402 citations
• Super-FAN: Integrated Facial Landmark Localization and Super-Resolution of Real-World Low Resolution Faces in Arbitrary Poses with GANs (2017) — 336 citations
• Factorized Higher-Order CNNs With an Application to Spatio-Temporal Emotion Estimation (2019) — 89 citations
• Knowledge distillation via adaptive instance normalization (2020) — 29 citations
• Toward fast and accurate human pose estimation via soft-gated skip connections (2020) — 96 citations
• Deep machine learning provides state-of-the-art performance in image-based plant phenotyping (2016) — 365 citations