Qinghua Hu

Computer Vision and Pattern Recognition · 2006–2020

A researcher in the Science corpus: 21 papers; trusts 1,009 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 452 citations into it; #8,435 of 21,575 by trust here, #4,286 by citations
• Artificial Intelligence — 214 citations into it; #11,811 of 35,345 by trust here, #10,065 by citations
• Media Technology — 60 citations into it
• Evaluation & Benchmarks — 47 citations into it
• Aerospace Engineering — 43 citations into it
• Model Architectures — 41 citations into it
• Computational Mechanics — 33 citations into it
• Computational Theory and Mathematics — 31 citations into it; #1,815 of 12,182 by trust here, #3,218 by citations

Whom Qinghua Hu trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yiyu Yao
5. Roman W. Swiniarski
6. Ross B. Girshick
7. Diederik P. Kingma
8. Huan Liu
9. Yoshua Bengio
10. Richard Jensen

Who trusts Qinghua Hu overall

1. Y. Qian
2. Daren Yu
3. Yiyu Yao
4. Richard Jensen
5. Jiye Liang
6. Pradipta Maji
7. Zongxia Xie
8. Jingtao Yao
9. Yan Zhao
10. Jinfu Liu

Papers from Qinghua Hu's perspective

Ordered by how strongly the researchers Qinghua Hu 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. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Vision Meets Drones: A Challenge (2018) — 533 citations
• ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks (2019) — 6,251 citations
• Progressive Image Deraining Networks: A Better and Simpler Baseline (2019) — 1,016 citations
• Neural Blind Deconvolution Using Deep Priors (2019) — 332 citations
• VisDrone-DET2019: The Vision Meets Drone Object Detection in Image Challenge Results (2019) — 1,010 citations
• Unsupervised feature selection by regularized self-representation (2015) — 315 citations
• Fuzzy probabilistic approximation spaces and their information measures (2006) — 316 citations
• Information-preserving hybrid data reduction based on fuzzy-rough techniques (2006) — 467 citations
• Neighborhood classifiers (2008) — 500 citations
• Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation (2007) — 415 citations
• Neighborhood rough set based heterogeneous feature subset selection (2008) — 1,012 citations
• Transfer learning for short-term wind speed prediction with deep neural networks (2016) — 428 citations
• Generalized Latent Multi-View Subspace Clustering (2020) — 650 citations
• Drone-Based RGB-Infrared Cross-Modality Vehicle Detection Via Uncertainty-Aware Learning (2020) — 617 citations
• Deep Partial Multi-View Learning (2020) — 315 citations
• Detection and Tracking Meet Drones Challenge (2020) — 1,156 citations
• Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation (2020) — 1,369 citations
• Wind Power Curve Modeling and Wind Power Forecasting With Inconsistent Data (2019) — 142 citations
• Latent Multi-view Subspace Clustering (2017) — 485 citations
• Short-Term Wind Speed or Power Forecasting With Heteroscedastic Support Vector Regression (2016) — 147 citations