Rajeev Ranjan

Computer Vision and Pattern Recognition · 2016–2019

A researcher in the Science corpus: 6 papers; trusts 1,031 researchers and is trusted by 57 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 160 citations into it; #3,784 of 21,575 by trust here, #4,137 by citations
• Signal Processing — 51 citations into it; #650 of 7,572 by trust here, #553 by citations
• Artificial Intelligence — 48 citations into it
• Safety, Robustness & Interpretability — 23 citations into it
• Evaluation & Benchmarks — 21 citations into it
• Model Architectures — 13 citations into it
• Representation & Self-Supervised Learning — 12 citations into it
• Aerospace Engineering — 7 citations into it

Whom Rajeev Ranjan trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Yann LeCun
5. Ross B. Girshick
6. Shaoqing Ren
7. Ilya Sutskever
8. Christian Szegedy
9. Yoshua Bengio
10. Karen Simonyan

Who trusts Rajeev Ranjan overall

1. Vishwanath A. Sindagi
2. Nataniel Ruiz
3. Hee Jung Ryu
4. Carlos Domingo Castillo
5. S. Sankaranarayanan
6. Feng Wang
7. Ramalingam Chellappa
8. Tsun-Yi Yang
9. Vishal M. Patel
10. Elliot Meyerson

Papers from Rajeev Ranjan's perspective

Ordered by how strongly the researchers Rajeev Ranjan 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. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• HyperFace: A Deep Multi-Task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition (2019) — 1,297 citations
• L2-constrained Softmax Loss for Discriminative Face Verification (2017) — 482 citations
• An All-In-One Convolutional Neural Network for Face Analysis (2016) — 450 citations
• UMDFaces: An annotated face dataset for training deep networks (2016) — 232 citations
• Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms (2018) — 361 citations
• Improving Network Robustness against Adversarial Attacks with Compact Convolution (2017) — 14 citations