Xiongwei Wu

Computer Vision and Pattern Recognition · with Hoi · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 1,230 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 238 citations into it; #18,763 of 21,575 by trust here, #16,367 by citations
• Artificial Intelligence — 107 citations into it; #30,664 of 35,345 by trust here, #34,213 by citations
• Model Architectures — 26 citations into it
• Aerospace Engineering — 14 citations into it
• Efficiency & Compression — 14 citations into it
• Media Technology — 12 citations into it
• Evaluation & Benchmarks — 10 citations into it
• Optimization & Training — 9 citations into it

Whom Xiongwei Wu trusts overall

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

1. Ross B. Girshick
2. Kaiming He
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Tsung-Yi Lin
6. Piotr Doll'ar
7. David G. Lowe
8. Christian Szegedy
9. Diederik P. Kingma
10. Yoshua Bengio

Who trusts Xiongwei Wu overall

1. Zhaowei Cai
2. Mohamed Loey
3. Nuno Vasconcelos
4. Gunasekaran Manogaran

Papers from Xiongwei Wu's perspective

Ordered by how strongly the researchers Xiongwei Wu 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. Gradient-based learning applied to document recognition
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Long Short-Term Memory
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Single-Shot Bidirectional Pyramid Networks for High-Quality Object Detection (2018) — 34 citations
• Recent Advances in Deep Learning for Object Detection (2019) — 947 citations