Feng Lu

Computer Vision and Pattern Recognition · 2018–2020

A researcher in the Science corpus: 7 papers; trusts 1,101 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 228 citations into it; #17,644 of 21,575 by trust here, #11,083 by citations
• Artificial Intelligence — 124 citations into it; #22,820 of 35,345 by trust here, #16,771 by citations
• Safety, Robustness & Interpretability — 70 citations into it
• Generative Models — 56 citations into it
• Media Technology — 53 citations into it
• Signal Processing — 26 citations into it
• Evaluation & Benchmarks — 20 citations into it
• Multimodal Learning — 19 citations into it

Whom Feng Lu trusts overall

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

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

Who trusts Feng Lu overall

1. Fanqing Lin
2. Chongyi Li
3. Feifan Lv
4. Christos Sakaridis
5. Lingzhi Zhang
6. Yufei Wang
7. Jiaying Liu
8. Hao Bian
9. Yige Li

Papers from Feng Lu's perspective

Ordered by how strongly the researchers Feng Lu 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
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

• CPGAN: Content-Parsing Generative Adversarial Networks for Text-to-Image Synthesis (2020) — 72 citations
• CPGAN: Full-Spectrum Content-Parsing Generative Adversarial Networks for Text-to-Image Synthesis (2019) — 19 citations
• Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems (2019) — 547 citations
• Generalizing Hand Segmentation in Egocentric Videos With Uncertainty-Guided Model Adaptation (2020) — 47 citations
• Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks (2020) — 617 citations
• Attention Guided Low-Light Image Enhancement with a Large Scale Low-Light Simulation Dataset (2019) — 330 citations
• MBLLEN: Low-Light Image/Video Enhancement Using CNNs (2018) — 794 citations