Heliang Zheng

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 5 papers; trusts 840 researchers and is trusted by 75 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 153 citations into it; #3,513 of 21,575 by trust here, #4,193 by citations
• Artificial Intelligence — 75 citations into it; #3,904 of 35,345 by trust here, #5,682 by citations
• Model Architectures — 27 citations into it; #1,098 of 2,278 by trust here, #1,145 by citations
• Aerospace Engineering — 20 citations into it
• Evaluation & Benchmarks — 16 citations into it
• Representation & Self-Supervised Learning — 12 citations into it
• Developmental Biology — 11 citations into it
• Ecology — 11 citations into it

Whom Heliang Zheng trusts overall

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

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

Who trusts Heliang Zheng overall

1. Fan Zhang
2. Jianlong Fu
3. Tao Hu
4. Fanjie Kong
5. Xiren Ma
6. Meike Nauta
7. Ruyi Ji
8. Yin Cui
9. Jinlin Guo
10. Xiu-Shen Wei

Papers from Heliang Zheng's perspective

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

Their papers

• Learning Multi-attention Convolutional Neural Network for Fine-Grained Image Recognition (2017) — 932 citations
• Look Closer to See Better: Recurrent Attention Convolutional Neural Network for Fine-Grained Image Recognition (2017) — 1,285 citations
• Learning Deep Bilinear Transformation for Fine-grained Image Representation (2019) — 178 citations
• Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-Grained Image Recognition (2019) — 436 citations
• Learning Rich Part Hierarchies With Progressive Attention Networks for Fine-Grained Image Recognition (2020) — 111 citations