Xiaoyu Wang

Computer Vision and Pattern Recognition · with Lin · 2012–2017

A researcher in the Science corpus: 6 papers; trusts 879 researchers and is trusted by 1,036 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 211 citations into it; #1,333 of 21,575 by trust here, #3,848 by citations
• Artificial Intelligence — 113 citations into it; #14,590 of 35,345 by trust here, #16,166 by citations
• Natural Language Processing — 27 citations into it
• Model Architectures — 21 citations into it
• Multimodal Learning — 21 citations into it
• Evaluation & Benchmarks — 19 citations into it
• Aerospace Engineering — 15 citations into it; #705 of 6,421 by trust here, #1,183 by citations
• Representation & Self-Supervised Learning — 14 citations into it

Whom Xiaoyu Wang trusts overall

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

1. Ross B. Girshick
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Kaiming He
5. Pedro F. Felzenszwalb
6. Ilya Sutskever
7. Yoshua Bengio
8. David G. Lowe
9. Diederik P. Kingma
10. Yann LeCun

Who trusts Xiaoyu Wang overall

1. Ross B. Girshick
2. Jan Hendrik Hosang
3. Kaiming He
4. Xiaodan Liang
5. Wanli Ouyang
6. Zhaowei Cai
7. Bernt Schiele
8. Wongun Choi
9. Rodrigo Benenson
10. Zhiqiang Shen

Papers from Xiaoyu Wang's perspective

Ordered by how strongly the researchers Xiaoyu 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. Long Short-Term Memory
5. ImageNet: A large-scale hierarchical image database
6. Gradient-based learning applied to document recognition
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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Regionlets for Generic Object Detection (2013) — 432 citations
• Generic Object Detection with Dense Neural Patterns and Regionlets (2014) — 67 citations
• Deep Reinforcement Learning-Based Image Captioning with Embedding Reward (2017) — 339 citations
• Hyper-class augmented and regularized deep learning for fine-grained image classification (2015) — 177 citations
• Deep Regionlets for Object Detection (2017) — 67 citations
• Histogram of Oriented Normal Vectors for Object Recognition with a Depth Sensor (2012) — 205 citations