Qing Liu

Computer Vision and Pattern Recognition · with Yuille · 2019–2020

A researcher in the Science corpus: 5 papers; trusts 567 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 111 citations into it; #16,259 of 21,575 by trust here, #14,478 by citations
• Artificial Intelligence — 96 citations into it; #26,596 of 35,345 by trust here, #21,878 by citations
• Representation & Self-Supervised Learning — 23 citations into it
• Optimization & Training — 21 citations into it
• Model Architectures — 16 citations into it; #2,192 of 2,278 by trust here, #2,078 by citations
• Evaluation & Benchmarks — 14 citations into it
• Media Technology — 6 citations into it
• Aerospace Engineering — 5 citations into it

Whom Qing Liu trusts overall

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

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

Who trusts Qing Liu overall

1. Lin Wang
2. Aneeshan Sain
3. Jianping Gou
4. Ayan Kumar Bhunia
5. Jiyang Qi
6. Kuk-Jin Yoon
7. Adam Kortylewski
8. Baosheng Yu

Papers from Qing Liu's perspective

Ordered by how strongly the researchers Qing Liu 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
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

• Compositional Convolutional Neural Networks: A Deep Architecture With Innate Robustness to Partial Occlusion (2020) — 110 citations
• Compositional Convolutional Neural Networks: A Robust and Interpretable Model for Object Recognition Under Occlusion (2020) — 90 citations
• Semantic-Aware Knowledge Preservation for Zero-Shot Sketch-Based Image Retrieval (2019) — 129 citations
• Combining Compositional Models and Deep Networks For Robust Object Classification under Occlusion (2019) — 74 citations
• Incremental Meta-Learning via Indirect Discriminant Alignment (2020) — 6 citations