Qingshan Liu

Computer Vision and Pattern Recognition · IEEE Transactions on Geoscience and Remote Sensing · 2014–2021

A researcher in the Science corpus: 8 papers; trusts 431 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 112 citations into it; #16,337 of 21,575 by trust here, #6,031 by citations
• Artificial Intelligence — 47 citations into it
• Media Technology — 45 citations into it; #3,007 of 3,621 by trust here, #1,329 by citations
• Atmospheric Science — 23 citations into it
• Model Architectures — 16 citations into it
• Aerospace Engineering — 11 citations into it
• Experimental and Cognitive Psychology — 9 citations into it
• Signal Processing — 8 citations into it

Whom Qingshan Liu trusts overall

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

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

Who trusts Qingshan Liu overall

1. Danfeng Hong
2. Chao Ma
3. Lichao Mou
4. Guangrui Zhao
5. Xin He
6. Yushi Chen
7. Zhetao Li
8. Seyed Mojtaba Marvasti-Zadeh
9. Lin Zhu

Papers from Qingshan Liu's perspective

Ordered by how strongly the researchers Qingshan 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
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. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Fast Visual Tracking via Dense Spatio-temporal Context Learning (2014) — 704 citations
• Learning Deep Global Multi-Scale and Local Attention Features for Facial Expression Recognition in the Wild (2021) — 320 citations
• Robust Lightweight Facial Expression Recognition Network with Label Distribution Training (2021) — 311 citations
• Classification of Hyperspectral and LiDAR Data Using Coupled CNNs (2020) — 367 citations
• Complementarity-Aware Attention Network for Salient Object Detection (2020) — 48 citations
• Cascaded Recurrent Neural Networks for Hyperspectral Image Classification (2017) — 680 citations
• Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images (2015) — 815 citations
• Robust Visual Tracking via Convolutional Networks Without Training (2015) — 354 citations