Jie Hu

Computer Vision and Pattern Recognition · with Sun · 2016–2021

A researcher in the Science corpus: 5 papers; trusts 864 researchers and is trusted by 1,957 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 256 citations into it; #906 of 21,575 by trust here, #703 by citations
• Artificial Intelligence — 168 citations into it; #1,283 of 35,345 by trust here, #872 by citations
• Model Architectures — 116 citations into it; #198 of 2,278 by trust here, #118 by citations
• Optimization & Training — 29 citations into it
• Efficiency & Compression — 21 citations into it
• Neural Architecture Search & AutoML — 20 citations into it
• Representation & Self-Supervised Learning — 20 citations into it
• Evaluation & Benchmarks — 14 citations into it

Whom Jie Hu trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Karen Simonyan
5. Ross B. Girshick
6. Christian Szegedy
7. Shaoqing Ren
8. Yoshua Bengio
9. Ilya Sutskever
10. Andrew Zisserman

Who trusts Jie Hu overall

1. Quoc V. Le
2. Liang-Chieh Chen
3. Irwan Bello
4. Meng-Hao Guo
5. Christoph Feichtenhofer
6. Jianwei Yang
7. Qibin Hou
8. Alan L. Yuille
9. Barret Zoph
10. Kai Han

Papers from Jie Hu's perspective

Ordered by how strongly the researchers Jie 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. 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

• Squeeze-and-Excitation Networks (2017) — 35,229 citations
• Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks (2018) — 700 citations
• Squeeze-and-Excitation Networks (2017) — 1,495 citations
• A Key Volume Mining Deep Framework for Action Recognition (2016) — 240 citations
• Involution: Inverting the Inherence of Convolution for Visual Recognition (2021) — 392 citations