B. V. Vijaya Kumar

Computer Vision and Pattern Recognition · 2002–2019

A researcher in the Science corpus: 9 papers; trusts 561 researchers and is trusted by 204 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 123 citations into it; #2,641 of 21,575 by trust here, #2,177 by citations
• Artificial Intelligence — 78 citations into it; #5,121 of 35,345 by trust here, #5,720 by citations
• Representation & Self-Supervised Learning — 35 citations into it; #1,818 of 3,145 by trust here, #1,597 by citations
• Evaluation & Benchmarks — 23 citations into it
• Optimization & Training — 16 citations into it
• Computational Mechanics — 14 citations into it
• Model Architectures — 10 citations into it
• Signal Processing — 10 citations into it; #1,932 of 7,572 by trust here, #3,198 by citations

Whom B. V. Vijaya Kumar trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. Diederik P. Kingma
6. Yann LeCun
7. Ross B. Girshick
8. Ilya Sutskever
9. Karen Simonyan
10. Aapo Hyvärinen

Who trusts B. V. Vijaya Kumar overall

1. Hamed Kiani Galoogahi
2. Vishal M. Patel
3. Luca Bertinetto
4. Chao Ma
5. Lukas Hoyer
6. Nalini K. Ratha
7. Dengxin Dai
8. Christos Sakaridis
9. Yahao Liu
10. Luc Van Gool

Papers from B. V. Vijaya Kumar's perspective

Ordered by how strongly the researchers B. V. Vijaya Kumar 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Long Short-Term Memory
8. ImageNet Large Scale Visual Recognition Challenge
9. Attention is All you Need (Neural Information Processing Systems)
10. Going deeper with convolutions

Their papers

• Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-training (2018) — 1,499 citations
• Confidence Regularized Self-Training (2019) — 917 citations
• One Network to Solve Them All — Solving Linear Inverse Problems Using Deep Projection Models (2017) — 359 citations
• Maximum Margin Correlation Filter: A New Approach for Localization and Classification (2013) — 102 citations
• Correlation Pattern Recognition (2002) — 402 citations
• Heartbeat Classification Using Morphological and Dynamic Features of ECG Signals (2012) — 488 citations
• Image Formation Process (2009) — 1,815 citations
• Cancelable biometric filters for face recognition (2004) — 319 citations
• Performance of the extended maximum average correlation height (EMACH) filter and the polynomial distance classifier correlation filter (PDCCF) for multiclass SAR detection and classification (2002) — 49 citations