Vijay Badrinarayanan

Computer Vision and Pattern Recognition · 2015–2021

A researcher in the Science corpus: 9 papers; trusts 1,257 researchers and is trusted by 2,363 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 253 citations into it; #810 of 21,575 by trust here, #893 by citations
• Artificial Intelligence — 106 citations into it; #945 of 35,345 by trust here, #1,186 by citations
• Aerospace Engineering — 56 citations into it; #5,765 of 6,421 by trust here, #2,086 by citations
• Model Architectures — 47 citations into it; #1,390 of 2,278 by trust here, #2,192 by citations
• 3D Vision & Pose — 31 citations into it; #651 of 689 by trust here, #449 by citations
• Computational Mechanics — 28 citations into it
• Optimization & Training — 25 citations into it; #1,171 of 2,601 by trust here, #878 by citations
• Evaluation & Benchmarks — 23 citations into it

Whom Vijay Badrinarayanan trusts overall

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

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

Who trusts Vijay Badrinarayanan overall

1. Alex Kendall
2. Liang-Chieh Chen
3. Yarin Gal
4. Xiaodan Liang
5. Andy Zeng
6. Abhishek Chaurasia
7. Yuhui Yuan
8. Yoshua Bengio
9. Jingdong Wang
10. Hengshuang Zhao

Papers from Vijay Badrinarayanan's perspective

Ordered by how strongly the researchers Vijay Badrinarayanan 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

• SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation (2015) — 18,199 citations
• GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks (2017) — 1,947 citations
• Understanding RealWorld Indoor Scenes with Synthetic Data (2015) — 237 citations
• Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding (2015) — 1,141 citations
• FIERY: Future Instance Prediction in Bird’s-Eye View from Surround Monocular Cameras (2021) — 344 citations
• Atlas: End-to-End 3D Scene Reconstruction from Posed Images (2020) — 349 citations
• Understanding symmetries in deep networks (2015) — 44 citations
• Gradient Adversarial Training of Neural Networks (2018) — 41 citations
• SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling (2015) — 836 citations