Dominic Zeng Wang

Computer Vision and Pattern Recognition · 2012–2018

A researcher in the Science corpus: 7 papers; trusts 325 researchers and is trusted by 202 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 76 citations into it; #2,564 of 21,575 by trust here, #4,344 by citations
• Artificial Intelligence — 41 citations into it
• Aerospace Engineering — 29 citations into it
• Automotive Engineering — 17 citations into it
• Model Architectures — 13 citations into it
• Reinforcement Learning — 10 citations into it
• Control and Systems Engineering — 9 citations into it
• Electrical and Electronic Engineering — 7 citations into it

Whom Dominic Zeng Wang trusts overall

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

1. Mark Everingham
2. Geoffrey E. Hinton
3. Pedro F. Felzenszwalb
4. Yoshua Bengio
5. Luc Van Gool
6. Kaiming He
7. Navneet Dalal
8. Alex Krizhevsky
9. Chih-Jen Lin
10. Yann LeCun

Who trusts Dominic Zeng Wang overall

1. Charles Ruizhongtai Qi
2. Lei Tai
3. Bo Li
4. Yin Zhou
5. Binh Yang
6. Julie Dequaire
7. Xiaozhi Chen
8. Martin Simon
9. Zetong Yang
10. Tianwei Yin

Papers from Dominic Zeng Wang's perspective

Ordered by how strongly the researchers Dominic Zeng Wang trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Voting for Voting in Online Point Cloud Object Detection (2015) — 378 citations
• Vote3Deep: Fast object detection in 3D point clouds using efficient convolutional neural networks (2016) — 541 citations
• What could move? Finding cars, pedestrians and bicyclists in 3D laser data (2012) — 120 citations
• Deep tracking in the wild: End-to-end tracking using recurrent neural networks (2018) — 112 citations
• Deep Tracking on the Move: Learning to Track the World from a Moving Vehicle using Recurrent Neural Networks (2016) — 20 citations
• Watch this: Scalable cost-function learning for path planning in urban environments (2016) — 120 citations
• Model-free detection and tracking of dynamic objects with 2D lidar (2015) — 88 citations