Di Feng

Computer Vision and Pattern Recognition · 2018–2020

A researcher in the Science corpus: 3 papers; trusts 579 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 219 citations into it; #15,853 of 21,575 by trust here, #9,435 by citations
• Artificial Intelligence — 91 citations into it
• Aerospace Engineering — 70 citations into it
• Automotive Engineering — 42 citations into it; #1,732 of 3,081 by trust here, #1,210 by citations
• Evaluation & Benchmarks — 39 citations into it
• Environmental Engineering — 38 citations into it; #3,869 of 5,479 by trust here, #3,203 by citations
• Probabilistic & Bayesian Methods — 30 citations into it
• Safety, Robustness & Interpretability — 17 citations into it

Whom Di Feng 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. Shaoqing Ren
6. Yann LeCun
7. Diederik P. Kingma
8. Yoshua Bengio
9. Alex Graves
10. Radford M. Neal

Who trusts Di Feng overall

1. Antonio Loquercio
2. Eduardo Arnold
3. Yongjian Chen
4. Jiwoong Choi
5. Jeremiah Zhe Liu
6. Yiming Sun
7. Ali Harakeh
8. Mattia Segu
9. Lei Tai
10. Dayoung Chun

Papers from Di Feng's perspective

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

Their papers

• Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2019) — 1,343 citations
• Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection (2018) — 283 citations
• A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving (2020) — 314 citations