Li Yuan

Computer Vision and Pattern Recognition · with Feng · 2019–2021

A researcher in the Science corpus: 12 papers; trusts 1,353 researchers and is trusted by 65 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 421 citations into it; #3,689 of 21,575 by trust here, #2,091 by citations
• Artificial Intelligence — 308 citations into it; #6,185 of 35,345 by trust here, #2,929 by citations
• Model Architectures — 144 citations into it; #1,042 of 2,278 by trust here, #415 by citations
• Optimization & Training — 57 citations into it
• Efficiency & Compression — 50 citations into it
• Evaluation & Benchmarks — 37 citations into it
• Representation & Self-Supervised Learning — 35 citations into it
• Meta & Continual Learning — 11 citations into it

Whom Li Yuan trusts overall

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

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

Who trusts Li Yuan overall

1. Weihao Yu
2. Qibin Hou
3. Daquan Zhou
4. Jianwei Yang
5. Tri Dao
6. Jiashi Feng
7. Zihang Jiang
8. Ali Hassani
9. Zhen Wang
10. Jianping Gou

Papers from Li Yuan's perspective

Ordered by how strongly the researchers Li Yuan 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. 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

• Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet (2021) — 2,523 citations
• VOLO: Vision Outlooker for Visual Recognition (2021) — 408 citations
• Distilling Object Detectors With Fine-Grained Feature Imitation (2019) — 476 citations
• All Tokens Matter: Token Labeling for Training Better Vision Transformers (2021) — 248 citations
• Central Similarity Quantization for Efficient Image and Video Retrieval (2019) — 374 citations
• Vision Permutator: A Permutable MLP-Like Architecture for Visual Recognition (2021) — 251 citations
• Revisit Knowledge Distillation: a Teacher-free Framework (2019) — 98 citations
• Revisiting Knowledge Distillation via Label Smoothing Regularization (2020) — 652 citations
• Continual Learning via Bit-Level Information Preserving (2021) — 54 citations
• A Simple Baseline for Pose Tracking in Videos of Crowed Scenes (2020) — 4 citations
• Toward Accurate Person-level Action Recognition in Videos of Crowed Scenes (2020) — 7 citations
• Few-Shot Adaptive Faster R-CNN (2019) — 151 citations