K. Li

Computer Vision and Pattern Recognition · with Li · 2008–2020

A researcher in the Science corpus: 6 papers; trusts 646 researchers and is trusted by 15,335 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 110 citations into it; #73 of 21,575 by trust here, #62 by citations
• Artificial Intelligence — 71 citations into it; #8,457 of 35,345 by trust here, #9,369 by citations
• Representation & Self-Supervised Learning — 24 citations into it
• Evaluation & Benchmarks — 23 citations into it; #17 of 3,147 by trust here, #6 by citations
• Multimodal Learning — 12 citations into it
• Media Technology — 11 citations into it
• Meta & Continual Learning — 11 citations into it
• Optimization & Training — 10 citations into it; #908 of 2,601 by trust here, #988 by citations

Whom K. Li trusts overall

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

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

Who trusts K. Li overall

1. Trevor Darrell
2. Yoshua Bengio
3. Kaiming He
4. Ross B. Girshick
5. Yi Yang
6. Chunhua Shen
7. Abhinav Kumar Gupta
8. Andrea Vedaldi
9. Luc Van Gool
10. Dacheng Tao

Papers from K. Li's perspective

Ordered by how strongly the researchers K. Li 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. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
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. Learning Multiple Layers of Features from Tiny Images

Their papers

• ImageNet: A large-scale hierarchical image database (2009) — 74,730 citations
• What Does Classifying More Than 10, 000 Image Categories Tell Us? (2010) — 636 citations
• Towards Scalable Dataset Construction: An Active Learning Approach (2008) — 146 citations
• Visual Semantic Reasoning for Image-Text Matching (2019) — 614 citations
• Efficient k-nearest neighbor graph construction for generic similarity measures (2011) — 841 citations
• Adversarial Feature Hallucination Networks for Few-Shot Learning (2020) — 302 citations