Tian Xia

Computer Vision and Pattern Recognition · with Wang · 2015–2016

A researcher in the Science corpus: 3 papers; trusts 361 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 83 citations into it; #12,708 of 21,575 by trust here, #5,082 by citations
• Artificial Intelligence — 40 citations into it; #20,416 of 35,345 by trust here, #7,062 by citations
• Representation & Self-Supervised Learning — 15 citations into it
• Model Architectures — 13 citations into it
• Evaluation & Benchmarks — 12 citations into it; #2,516 of 3,147 by trust here, #1,196 by citations
• Aerospace Engineering — 8 citations into it
• Developmental Biology — 5 citations into it
• Ecology — 5 citations into it

Whom Tian Xia trusts overall

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

1. Ross B. Girshick
2. Karen Simonyan
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Ronald J. Williams
6. Andrew Zisserman
7. Kaiming He
8. Christian Szegedy
9. Yann LeCun
10. Ilya Sutskever

Who trusts Tian Xia overall

1. Heliang Zheng
2. Arash Vahdat
3. Jianlong Fu
4. Xingjun Ma
5. Yisen Wang
6. Kun Yi
7. Junnan Li
8. Ryuichiro Hataya
9. Eran Malach
10. Dan Hendrycks

Papers from Tian Xia's perspective

Ordered by how strongly the researchers Tian Xia 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. ImageNet: A large-scale hierarchical image database
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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

• Learning from massive noisy labeled data for image classification (2015) — 1,303 citations
• Fully Convolutional Attention Localization Networks: Efficient Attention Localization for Fine-Grained Recognition (2016) — 102 citations
• Fully Convolutional Attention Networks for Fine-Grained Recognition (2016) — 155 citations