Rajeev Yasarla

Computer Vision and Pattern Recognition · 2019–2021

A researcher in the Science corpus: 3 papers; trusts 716 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 118 citations into it; #10,405 of 21,575 by trust here, #15,955 by citations
• Artificial Intelligence — 52 citations into it
• Media Technology — 24 citations into it; #2,853 of 3,621 by trust here, #3,522 by citations
• Model Architectures — 18 citations into it
• Generative Models — 7 citations into it
• Safety, Risk, Reliability and Quality — 7 citations into it
• Computer Graphics and Computer-Aided Design — 5 citations into it; #859 of 1,584 by trust here, #1,116 by citations
• Signal Processing — 5 citations into it

Whom Rajeev Yasarla trusts overall

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

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

Who trusts Rajeev Yasarla overall

1. Chong Mou
2. Vishwanath A. Sindagi
3. Syed Waqas Zamir
4. Kui Jiang
5. Ozan Özdenizci
6. Qian Wang
7. Aditya Arora
8. Vishal M. Patel
9. Zhongyuan Wang
10. Jian Zhang

Papers from Rajeev Yasarla's perspective

Ordered by how strongly the researchers Rajeev Yasarla 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. Attention is All you Need (Neural Information Processing Systems)
5. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• TransWeather: Transformer-based Restoration of Images Degraded by Adverse Weather Conditions (2021) — 524 citations
• JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method (2020) — 337 citations
• Uncertainty Guided Multi-Scale Residual Learning-Using a Cycle Spinning CNN for Single Image De-Raining (2019) — 338 citations