Hang Zhang

Computer Vision and Pattern Recognition · with Zhang · 2017–2022

A researcher in the Science corpus: 8 papers; trusts 1,005 researchers and is trusted by 276 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 264 citations into it; #2,419 of 21,575 by trust here, #1,857 by citations
• Artificial Intelligence — 156 citations into it; #33,814 of 35,345 by trust here, #6,517 by citations
• Model Architectures — 57 citations into it
• Optimization & Training — 40 citations into it
• Evaluation & Benchmarks — 30 citations into it
• Generative Models — 20 citations into it; #766 of 1,956 by trust here, #1,355 by citations
• Computer Graphics and Computer-Aided Design — 14 citations into it
• Representation & Self-Supervised Learning — 14 citations into it

Whom Hang Zhang trusts overall

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

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

Who trusts Hang Zhang overall

1. Zizhao Zhang
2. Yongcheng Jing
3. Qibin Hou
4. Jun Fu
5. Hugo Touvron
6. He Zhang
7. Yehao Li
8. Vishal M. Patel
9. Tong He
10. Hanchao Li

Papers from Hang Zhang's perspective

Ordered by how strongly the researchers Hang Zhang 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. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
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

• Bag of Tricks for Image Classification with Convolutional Neural Networks (2018) — 1,617 citations
• Context Encoding for Semantic Segmentation (2018) — 1,368 citations
• ResNeSt: Split-Attention Networks (2020) — 1,747 citations
• Multi-style Generative Network for Real-time Transfer (2017) — 306 citations
• Bag of Freebies for Training Object Detection Neural Networks (2019) — 203 citations
• Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP (2022) — 767 citations
• AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data (2020) — 995 citations
• Character Matters: Video Story Understanding with Character-Aware Relations (2020) — 11 citations