Zhicheng Yan

Computer Vision and Pattern Recognition · 2014–2022

A researcher in the Science corpus: 13 papers; trusts 1,401 researchers and is trusted by 242 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 465 citations into it; #2,486 of 21,575 by trust here, #2,228 by citations
• Artificial Intelligence — 279 citations into it; #20,387 of 35,345 by trust here, #4,080 by citations
• Model Architectures — 108 citations into it; #1,993 of 2,278 by trust here, #874 by citations
• Evaluation & Benchmarks — 63 citations into it
• Optimization & Training — 45 citations into it
• Representation & Self-Supervised Learning — 33 citations into it
• Efficiency & Compression — 17 citations into it
• Endocrinology, Diabetes and Metabolism — 14 citations into it

Whom Zhicheng Yan trusts overall

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

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

Who trusts Zhicheng Yan overall

1. Karim Ahmed
2. Liang-Chieh Chen
3. Lorenzo Torresani
4. Peter Hase
5. Weixin Liang
6. Yu-Gang Jiang
7. Anastasia Ioannidou
8. Yuki Endo
9. Wencheng Wang
10. Calvin Murdock

Papers from Zhicheng Yan's perspective

Ordered by how strongly the researchers Zhicheng Yan 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. Long Short-Term Memory
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

• Multiscale Vision Transformers (2021) — 1,678 citations
• Decoupling Representation and Classifier for Long-Tailed Recognition (2019) — 1,567 citations
• Automatic Photo Adjustment Using Deep Neural Networks (2014) — 298 citations
• SLAC: A Sparsely Labeled Dataset for Action Classification and Localization (2017) — 31 citations
• Graph-Based Global Reasoning Networks (2018) — 506 citations
• HACS: Human Action Clips and Segments Dataset for Recognition and Temporal Localization (2017) — 305 citations
• HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition (2014) — 483 citations
• Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks With Octave Convolution (2019) — 687 citations
• Combining the Best of Convolutional Layers and Recurrent Layers: A Hybrid Network for Semantic Segmentation (2016) — 42 citations
• HD-CNN: Hierarchical Deep Convolutional Neural Network for Image Classification (2014) — 65 citations
• 3rd Continual Learning Workshop Challenge on Egocentric Category and Instance Level Object Understanding (2022) — 7 citations
• Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective (2020)
• Only Time Can Tell: Discovering Temporal Data for Temporal Modeling (2019) — 98 citations