Zhenda Xie

Computer Vision and Pattern Recognition · with Lin · 2019–2022

A researcher in the Science corpus: 7 papers; trusts 878 researchers and is trusted by 127 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 259 citations into it; #3,053 of 21,575 by trust here, #2,519 by citations
• Artificial Intelligence — 202 citations into it; #9,592 of 35,345 by trust here, #3,539 by citations
• Model Architectures — 72 citations into it; #1,120 of 2,278 by trust here, #539 by citations
• Representation & Self-Supervised Learning — 70 citations into it
• Optimization & Training — 29 citations into it
• Evaluation & Benchmarks — 25 citations into it
• Electrical and Electronic Engineering — 10 citations into it
• Signal Processing — 6 citations into it

Whom Zhenda Xie trusts overall

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

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

Who trusts Zhenda Xie overall

1. Yongming Rao
2. Ze Liu
3. Jinghao Zhou
4. Yulin Wang
5. Chien-Yao Wang
6. Han Hu
7. Jianwei Yang
8. Zheng Zhang
9. Jie Gui
10. Yue Cao

Papers from Zhenda Xie's perspective

Ordered by how strongly the researchers Zhenda Xie trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
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

• Local Relation Networks for Image Recognition (2019) — 564 citations
• Swin Transformer V2: Scaling Up Capacity and Resolution (2021) — 2,875 citations
• SimMIM: a Simple Framework for Masked Image Modeling (2021) — 1,859 citations
• Self-Supervised Learning with Swin Transformers (2021) — 209 citations
• Contrastive Learning Rivals Masked Image Modeling in Fine-tuning via Feature Distillation (2022) — 146 citations
• Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning (2020) — 477 citations
• Spatially Adaptive Inference with Stochastic Feature Sampling and Interpolation (2020) — 103 citations