Abhishek Sharma

Computer Vision and Pattern Recognition · with Jacobs · 2011–2017

A researcher in the Science corpus: 6 papers; trusts 792 researchers and is trusted by 321 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 102 citations into it; #2,247 of 21,575 by trust here, #4,623 by citations
• Artificial Intelligence — 35 citations into it; #9,798 of 35,345 by trust here, #12,387 by citations
• Natural Language Processing — 11 citations into it
• Representation & Self-Supervised Learning — 11 citations into it; #1,516 of 3,145 by trust here, #1,722 by citations
• Signal Processing — 10 citations into it
• Model Architectures — 9 citations into it; #1,490 of 2,278 by trust here, #1,794 by citations
• Multimodal Learning — 9 citations into it; #304 of 1,211 by trust here, #637 by citations

Whom Abhishek Sharma trusts overall

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

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

Who trusts Abhishek Sharma overall

1. Kaiye Wang
2. Cuicui Kang
3. Changxing Ding
4. Gabriella Csurka
5. Dacheng Tao
6. Peng Hu
7. Liangli Zhen
8. Nicolas Courty
9. Lefei Zhang
10. Shengcai Liao

Papers from Abhishek Sharma's perspective

Ordered by how strongly the researchers Abhishek 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
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

• A Sentence Is Worth a Thousand Pixels (2013) — 56 citations
• Generalized Multiview Analysis: A discriminative latent space (2012) — 745 citations
• Locally Scale-Invariant Convolutional Neural Networks (2014) — 139 citations
• Deep hierarchical parsing for semantic segmentation (2015) — 117 citations
• R-FCN-3000 at 30fps: Decoupling Detection and Classification (2017) — 98 citations
• Bypassing synthesis: PLS for face recognition with pose, low-resolution and sketch (2011) — 396 citations