Yutong Lin

Computer Vision and Pattern Recognition · 2020–2021

A researcher in the Science corpus: 7 papers; trusts 883 researchers and is trusted by 634 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 310 citations into it; #1,703 of 21,575 by trust here, #832 by citations
• Artificial Intelligence — 234 citations into it; #3,799 of 35,345 by trust here, #1,046 by citations
• Representation & Self-Supervised Learning — 82 citations into it
• Model Architectures — 80 citations into it; #455 of 2,278 by trust here, #155 by citations
• Optimization & Training — 38 citations into it
• Evaluation & Benchmarks — 31 citations into it
• Natural Language Processing — 15 citations into it
• Electrical and Electronic Engineering — 10 citations into it

Whom Yutong Lin trusts overall

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

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

Who trusts Yutong Lin overall

1. Jianwei Yang
2. Yongming Rao
3. Wenliang Zhao
4. Weihao Yu
5. Meng-Hao Guo
6. Xueyan Zou
7. Yanghao Li
8. Salman Hameed Khan
9. Zhenda Xie
10. Chunyuan Li

Papers from Yutong Lin's perspective

Ordered by how strongly the researchers Yutong Lin 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

• Swin Transformer: Hierarchical Vision Transformer using Shifted Windows (2021) — 33,967 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
• Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning (2020) — 477 citations
• Negative Margin Matters: Understanding Margin in Few-shot Classification (2020) — 380 citations
• A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-Language Model (2021) — 346 citations