Wenjie Luo
Computer Vision and Pattern Recognition · 2016–2019
A researcher in the Science corpus: 9 papers; trusts 1,033 researchers and is trusted by 999 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 162 citations into it; #1,367 of 21,575 by trust here, #1,669 by citations
- Artificial Intelligence — 74 citations into it; #22,968 of 35,345 by trust here, #6,422 by citations
- Model Architectures — 24 citations into it; #1,578 of 2,278 by trust here, #635 by citations
- Natural Language Processing — 24 citations into it
- Automotive Engineering — 23 citations into it; #40 of 3,081 by trust here, #520 by citations
- Aerospace Engineering — 22 citations into it; #1,183 of 6,421 by trust here, #1,738 by citations
- Evaluation & Benchmarks — 21 citations into it
- Optimization & Training — 13 citations into it
Whom Wenjie Luo trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Ross B. Girshick
- Geoffrey E. Hinton
- Alex Krizhevsky
- Diederik P. Kingma
- Shaoqing Ren
- Yann LeCun
- Yoshua Bengio
- Karen Simonyan
- Ilya Sutskever
Who trusts Wenjie Luo overall
- Sergio Casas
- Ming Liang
- Binh Yang
- Raquel Urtasun
- Shaoshuai Shi
- Qi Chen
- Rowel O. Atienza
- Fatma Güney
- Andreas Geiger
- Wei Xiang
Papers from Wenjie Luo's perspective
Ordered by how strongly the researchers Wenjie Luo trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- Understanding the Effective Receptive Field in Deep Convolutional Neural Networks (2016) — 2,267 citations
- PIXOR: Real-time 3D Object Detection from Point Clouds (2018) — 1,233 citations
- Efficient Deep Learning for Stereo Matching (2016) — 777 citations
- Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net (2018) — 687 citations
- IntentNet: Learning to Predict Intention from Raw Sensor Data (2018) — 420 citations
- End-To-End Interpretable Neural Motion Planner (2019) — 482 citations
- TorontoCity: Seeing the World with a Million Eyes (2016) — 186 citations
- Efficient Summarization with Read-Again and Copy Mechanism (2016) — 101 citations
- DeepRoadMapper: Extracting Road Topology from Aerial Images (2017) — 489 citations