Qingjie Liu
Computer Vision and Pattern Recognition · 2017–2021
A researcher in the Science corpus: 4 papers; trusts 588 researchers and is trusted by 27 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 123 citations into it; #19,120 of 21,575 by trust here, #14,478 by citations
- Artificial Intelligence — 46 citations into it; #31,207 of 35,345 by trust here, #30,347 by citations
- Media Technology — 31 citations into it; #691 of 3,621 by trust here, #1,046 by citations
- Model Architectures — 26 citations into it; #1,980 of 2,278 by trust here, #2,128 by citations
- Evaluation & Benchmarks — 10 citations into it
- Optimization & Training — 9 citations into it
- Safety, Risk, Reliability and Quality — 9 citations into it
- Ocean Engineering — 7 citations into it; #251 of 3,193 by trust here, #601 by citations
Whom Qingjie Liu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Geoffrey E. Hinton
- Alex Krizhevsky
- Ross B. Girshick
- Diederik P. Kingma
- Shaoqing Ren
- Yoshua Bengio
- Yann LeCun
- Christian Szegedy
- X. Zhang
Who trusts Qingjie Liu overall
- Debesh Jha
- Md. Zahangir Alom
- Jeya Maria Jose Valanarasu
- Weihao Weng
- Pia Helen Smedsrud
- Qing Xu
- Ange Lou
- Lichen Zhou
- Xingyi Yang
- Botao Ye
Papers from Qingjie Liu's perspective
Ordered by how strongly the researchers Qingjie Liu trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- Visual Grounding with Transformers (2021) — 86 citations
- STMTrack: Template-free Visual Tracking with Space-time Memory Networks (2021) — 337 citations
- Remote Sensing Image Fusion Based on Two-Stream Fusion Network (2017) — 389 citations
- Road Extraction by Deep Residual U-Net (2017) — 2,740 citations