Yandong Guo

Computer Vision and Pattern Recognition · 2016–2023

A researcher in the Science corpus: 7 papers; trusts 578 researchers and is trusted by 314 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 135 citations into it; #2,290 of 21,575 by trust here, #1,538 by citations
• Artificial Intelligence — 94 citations into it; #5,773 of 35,345 by trust here, #3,229 by citations
• Representation & Self-Supervised Learning — 34 citations into it
• Signal Processing — 26 citations into it; #2,221 of 7,572 by trust here, #4,560 by citations
• Evaluation & Benchmarks — 19 citations into it
• Optimization & Training — 16 citations into it
• Model Architectures — 15 citations into it
• Meta & Continual Learning — 14 citations into it; #118 of 1,041 by trust here, #36 by citations

Whom Yandong Guo trusts overall

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

1. Alex Krizhevsky
2. Gary B. Huang
3. Kaiming He
4. Geoffrey E. Hinton
5. Ross B. Girshick
6. Robert Tibshirani
7. Diederik P. Kingma
8. Ilya Sutskever
9. Yoshua Bengio
10. Yann LeCun

Who trusts Yandong Guo overall

1. Mei Wang
2. Yue Wu
3. Jiankang Deng
4. Xi Yin
5. Ran He
6. Xiang Wu
7. Insaf Adjabi
8. Weihong Deng
9. Qiang Meng
10. Weiyang Liu

Papers from Yandong Guo's perspective

Ordered by how strongly the researchers Yandong Guo 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Large Scale Incremental Learning (2019) — 1,535 citations
• MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition (2016) — 2,204 citations
• MS-Celeb-1M: Challenge of Recognizing One Million Celebrities in the Real World (2016) — 92 citations
• One-shot Face Recognition by Promoting Underrepresented Classes (2017) — 108 citations
• Recognize Anything: A Strong Image Tagging Model (2023) — 432 citations
• ViDA: Homeostatic Visual Domain Adapter for Continual Test Time Adaptation (2023) — 80 citations
• Incremental Classifier Learning with Generative Adversarial Networks (2018) — 117 citations