Seyed Hamid Rezatofighi

Computer Vision and Pattern Recognition · 2010–2019

A researcher in the Science corpus: 9 papers; trusts 706 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 178 citations into it; #8,350 of 21,575 by trust here, #3,239 by citations
• Artificial Intelligence — 106 citations into it; #12,328 of 35,345 by trust here, #4,041 by citations
• Model Architectures — 25 citations into it
• Evaluation & Benchmarks — 24 citations into it; #2,739 of 3,147 by trust here, #847 by citations
• Probabilistic & Bayesian Methods — 21 citations into it
• Statistics and Probability — 11 citations into it
• Aerospace Engineering — 9 citations into it
• Signal Processing — 9 citations into it

Whom Seyed Hamid Rezatofighi trusts overall

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

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

Who trusts Seyed Hamid Rezatofighi overall

1. Yan Zhang
2. Anton Milan
3. Hamid Rezatofighi
4. Amaia Salvador
5. Xue Yang
6. Xingyi Zhou
7. Jonathon S. Hare
8. Chien-Yao Wang
9. Juan R. Terven
10. Shilong Liu

Papers from Seyed Hamid Rezatofighi's perspective

Ordered by how strongly the researchers Seyed Hamid Rezatofighi 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
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

• Generalized Intersection Over Union: A Metric and a Loss for Bounding Box Regression (2019) — 5,784 citations
• Online Multi-Target Tracking Using Recurrent Neural Networks (2016) — 537 citations
• DeepSetNet: Predicting Sets with Deep Neural Networks (2016) — 56 citations
• Deep Perm-Set Net: Learn to predict sets with unknown permutation and cardinality using deep neural networks (2018) — 26 citations
• Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks (2019) — 738 citations
• Joint Learning of Set Cardinality and State Distribution (2017) — 16 citations
• Efficient Point Process Inference for Large-Scale Object Detection (2016) — 24 citations
• Joint Probabilistic Data Association Revisited (2015) — 365 citations
• Automatic Recognition of Five Types of White Blood Cells in Peripheral Blood (2010) — 406 citations