Abhinav Valada

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 3 papers; trusts 975 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 100 citations into it; #16,878 of 21,575 by trust here, #13,815 by citations
• Artificial Intelligence — 39 citations into it
• Model Architectures — 15 citations into it
• Evaluation & Benchmarks — 11 citations into it
• Aerospace Engineering — 8 citations into it; #4,692 of 6,421 by trust here, #3,764 by citations
• Optimization & Training — 6 citations into it

Whom Abhinav Valada trusts overall

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

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

Who trusts Abhinav Valada overall

1. Fahad Lateef
2. Christos Sakaridis
3. Sorin Mihai Grigorescu
4. Roy Shilkrot
5. Lorenz Wellhausen
6. Yassine Ruichek
7. Shervin Minaee
8. Dengxin Dai
9. Supreeth Narasimhaswamy
10. Bogdan Trasnea

Papers from Abhinav Valada's perspective

Ordered by how strongly the researchers Abhinav Valada trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
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

• EfficientPS: Efficient Panoptic Segmentation (2020) — 260 citations
• SMSnet: Semantic motion segmentation using deep convolutional neural networks (2017) — 68 citations
• AdapNet: Adaptive semantic segmentation in adverse environmental conditions (2017) — 213 citations