Vivek Sharma

Computer Vision and Pattern Recognition · with Diba · 2016–2020

A researcher in the Science corpus: 11 papers; trusts 1,441 researchers and is trusted by 56 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 304 citations into it; #3,934 of 21,575 by trust here, #5,941 by citations
• Artificial Intelligence — 215 citations into it; #9,529 of 35,345 by trust here, #8,283 by citations
• Representation & Self-Supervised Learning — 39 citations into it; #3,112 of 3,145 by trust here, #1,982 by citations
• Evaluation & Benchmarks — 33 citations into it
• Model Architectures — 29 citations into it
• Federated & Distributed Learning — 20 citations into it
• Privacy & Security — 20 citations into it
• Signal Processing — 19 citations into it

Whom Vivek Sharma trusts overall

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

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

Who trusts Vivek Sharma overall

1. Ali Diba
2. Iker Ceballos
3. Abhishek Singh
4. Rohit Girdhar
5. Luc Van Gool
6. Fang Wan
7. Christoph Feichtenhofer
8. Praneeth Vepakomma
9. Rainer Stiefelhagen
10. Dongliang He

Papers from Vivek Sharma's perspective

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

Their papers

• Weakly Supervised Cascaded Convolutional Networks (2016) — 312 citations
• Deep Temporal Linear Encoding Networks (2016) — 240 citations
• DynamoNet: Dynamic Action and Motion Network (2019) — 124 citations
• Spatio-Temporal Channel Correlation Networks for Action Classification (2018) — 197 citations
• Deep Multimodal Feature Encoding for Video Ordering (2020) — 11 citations
• Classification-Driven Dynamic Image Enhancement (2017) — 71 citations
• ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations (2019) — 11 citations
• DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networks (2020) — 63 citations
• SplitNN-driven Vertical Partitioning (2020) — 91 citations
• ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries (2019) — 7 citations
• Holistic Large Scale Video Understanding (2019) — 37 citations