Tsung-Yu Lin

Computer Vision and Pattern Recognition · with Maji · 2015

A researcher in the Science corpus: 3 papers; trusts 1,146 researchers and is trusted by 972 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 155 citations into it; #1,383 of 21,575 by trust here, #3,598 by citations
• Artificial Intelligence — 52 citations into it; #2,512 of 35,345 by trust here, #5,059 by citations
• Aerospace Engineering — 16 citations into it
• Media Technology — 14 citations into it
• Model Architectures — 14 citations into it
• Computer Graphics and Computer-Aided Design — 11 citations into it
• Evaluation & Benchmarks — 8 citations into it
• Generative Models — 7 citations into it

Whom Tsung-Yu Lin trusts overall

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

1. Andrea Vedaldi
2. Karen Simonyan
3. Alex Krizhevsky
4. Andrew Zisserman
5. Geoffrey E. Hinton
6. Ross B. Girshick
7. David G. Lowe
8. Kaiming He
9. Ilya Sutskever
10. Christian Szegedy

Who trusts Tsung-Yu Lin overall

1. Heliang Zheng
2. Subhransu Maji
3. Yin Cui
4. Jianlong Fu
5. Simon Kornblith
6. Zhou Yu
7. Dacheng Tao
8. Yuxin Peng
9. Peiqin Zhuang
10. Xiu-Shen Wei

Papers from Tsung-Yu Lin's perspective

Ordered by how strongly the researchers Tsung-Yu Lin 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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Bilinear CNN Models for Fine-Grained Visual Recognition (2015) — 2,076 citations
• Visualizing and Understanding Deep Texture Representations (2015) — 146 citations
• Bilinear CNNs for Fine-grained Visual Recognition (2015) — 97 citations