Sadeep Jayasumana

Computer Vision and Pattern Recognition · 2013–2023

A researcher in the Science corpus: 6 papers; trusts 928 researchers and is trusted by 88 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 171 citations into it; #8,400 of 21,575 by trust here, #7,750 by citations
• Artificial Intelligence — 87 citations into it; #3,121 of 35,345 by trust here, #2,097 by citations
• Model Architectures — 30 citations into it; #312 of 2,278 by trust here, #227 by citations
• Evaluation & Benchmarks — 15 citations into it
• Probabilistic & Bayesian Methods — 14 citations into it
• Media Technology — 13 citations into it; #1,847 of 3,621 by trust here, #2,280 by citations
• Natural Language Processing — 9 citations into it; #1,406 of 5,688 by trust here, #565 by citations
• Computational Mechanics — 8 citations into it

Whom Sadeep Jayasumana trusts overall

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

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

Who trusts Sadeep Jayasumana overall

1. Anurag Arnab
2. Liang-Chieh Chen
3. Philip H. S. Torr
4. Xiaodan Liang
5. Chunhua Shen
6. Iasonas Kokkinos
7. Mehrtash Harandi
8. Hengshuang Zhao
9. Xiaogang Wang
10. Luc Van Gool

Papers from Sadeep Jayasumana's perspective

Ordered by how strongly the researchers Sadeep Jayasumana 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. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
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

• Conditional Random Fields as Recurrent Neural Networks (2015) — 2,597 citations
• Higher Order Conditional Random Fields in Deep Neural Networks (2015) — 232 citations
• Kernel Methods on the Riemannian Manifold of Symmetric Positive Definite Matrices (2013) — 312 citations
• Rethinking FID: Towards a Better Evaluation Metric for Image Generation (2023) — 396 citations
• Long-tail learning via logit adjustment (2020) — 1,020 citations
• Higher Order Potentials in End-to-End Trainable Conditional Random Fields (2015) — 23 citations