Tyng-Luh Liu

Computer Vision and Pattern Recognition · 2005–2021

A researcher in the Science corpus: 6 papers; trusts 418 researchers and is trusted by 54 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 93 citations into it; #3,798 of 21,575 by trust here, #6,542 by citations
• Artificial Intelligence — 59 citations into it
• Representation & Self-Supervised Learning — 44 citations into it
• Media Technology — 8 citations into it; #2,123 of 3,621 by trust here, #2,954 by citations
• Optimization & Training — 6 citations into it
• Signal Processing — 6 citations into it
• Evaluation & Benchmarks — 5 citations into it

Whom Tyng-Luh Liu trusts overall

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

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

Who trusts Tyng-Luh Liu overall

1. Deng Cai
2. Guanbin Li
3. Xi Li
4. Ming-Ming Cheng
5. Florian Jaton
6. Saining Xie
7. Meng Yang
8. Houwen Peng
9. Xiaofei He
10. Huchuan Lu

Papers from Tyng-Luh Liu's perspective

Ordered by how strongly the researchers Tyng-Luh Liu 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. 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. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
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

• Pixel-wise Deep Learning for Contour Detection (2015) — 110 citations
• Fusing generic objectness and visual saliency for salient object detection (2011) — 390 citations
• Local discriminant embedding and its variants (2005) — 625 citations
• Decoupled Contrastive Learning (2021) — 259 citations
• Semantic manifold learning for image retrieval (2005) — 117 citations
• Adaptive and Generative Zero-Shot Learning (2021) — 81 citations