B. V. Kumar

Computer Vision and Pattern Recognition · 2015–2018

A researcher in the Science corpus: 6 papers; trusts 761 researchers and is trusted by 73 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 147 citations into it; #3,788 of 21,575 by trust here, #3,875 by citations
• Artificial Intelligence — 72 citations into it; #13,184 of 35,345 by trust here, #11,490 by citations
• Representation & Self-Supervised Learning — 29 citations into it
• Aerospace Engineering — 18 citations into it
• Model Architectures — 18 citations into it
• Evaluation & Benchmarks — 15 citations into it
• Radiology, Nuclear Medicine and Imaging — 13 citations into it
• Media Technology — 9 citations into it; #541 of 3,621 by trust here, #258 by citations

Whom B. V. Kumar trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. David G. Lowe
4. Karen Simonyan
5. Andrew Zisserman
6. Kaiming He
7. Ilya Sutskever
8. Ross B. Girshick
9. Yann LeCun
10. Yoshua Bengio

Who trusts B. V. Kumar overall

1. Shiv Ram Dubey
2. Xu Zhang
3. Sofia Ira Ktena
4. Yurun Tian
5. Clément Godard
6. Kun-Juan Wei
7. Ben Harwood
8. Jamie Watson
9. Sarah Parisot
10. Alex Kendall

Papers from B. V. Kumar's perspective

Ordered by how strongly the researchers B. V. 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. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
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

• Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue (2016) — 1,652 citations
• Smart Mining for Deep Metric Learning (2017) — 373 citations
• Cartman: The Low-Cost Cartesian Manipulator that Won the Amazon Robotics Challenge (2017) — 147 citations
• Learning Local Image Descriptors with Deep Siamese and Triplet Convolutional Networks by Minimizing Global Loss Functions (2015) — 320 citations
• DeepSetNet: Predicting Sets with Deep Neural Networks (2016) — 56 citations
• Multi-modal Cycle-consistent Generalized Zero-Shot Learning (2018) — 386 citations