Nikhila Ravi

Computer Vision and Pattern Recognition · 2019–2023

A researcher in the Science corpus: 3 papers; trusts 387 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 161 citations into it; #18,778 of 21,575 by trust here, #3,713 by citations
• Artificial Intelligence — 51 citations into it
• 3D Vision & Pose — 31 citations into it; #648 of 689 by trust here, #437 by citations
• Computational Mechanics — 31 citations into it
• Model Architectures — 15 citations into it
• Aerospace Engineering — 14 citations into it
• Biomedical Engineering — 10 citations into it
• Human-Computer Interaction — 10 citations into it

Whom Nikhila Ravi trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Ross B. Girshick
4. Geoffrey E. Hinton
5. Karen Simonyan
6. Shaoqing Ren
7. Jitendra Malik
8. Ilya Sutskever
9. Diederik P. Kingma
10. Yann LeCun

Who trusts Nikhila Ravi overall

1. Chaozheng Wu
2. Michael J. Black
3. Heewoo Jun
4. Hanbyul Joo
5. Mathis Petrovich
6. Radek Daněček
7. Yuliang Xiu
8. Justin Johnson
9. Lukas Höllein
10. Abdullah Hamdi

Papers from Nikhila Ravi's perspective

Ordered by how strongly the researchers Nikhila Ravi 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. Gradient-based learning applied to document recognition
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

• Segment Anything (2023) — 14,627 citations
• Accelerating 3D deep learning with PyTorch3D (2019) — 1,177 citations
• C3DPO: Canonical 3D Pose Networks for Non-Rigid Structure From Motion (2019) — 129 citations