Jingjing Li

Computer Vision and Pattern Recognition · IEEE International Conference on Computer Vision · 2019–2021

A researcher in the Science corpus: 5 papers; trusts 463 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 105 citations into it; #16,340 of 21,575 by trust here, #12,419 by citations
• Artificial Intelligence — 97 citations into it; #30,810 of 35,345 by trust here, #17,776 by citations
• Representation & Self-Supervised Learning — 46 citations into it
• Information Systems — 17 citations into it; #7,326 of 8,771 by trust here, #5,942 by citations
• Model Architectures — 16 citations into it
• Generative Models — 10 citations into it
• Graph Learning — 10 citations into it
• Management Science and Operations Research — 7 citations into it

Whom Jingjing Li trusts overall

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

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

Who trusts Jingjing Li overall

1. Deng-Ping Fan
2. Vinay Kumar Verma
3. Jiahua Dong
4. Zhangxuan Gu
5. Tianwen Chen
6. Keren Fu
7. Zhengyi Liu
8. Shoujin Wang
9. Ruihong Qiu
10. Nian Liu

Papers from Jingjing Li's perspective

Ordered by how strongly the researchers Jingjing 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
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

• Leveraging the Invariant Side of Generative Zero-Shot Learning (2019) — 338 citations
• Cross-Domain Gradient Discrepancy Minimization for Unsupervised Domain Adaptation (2021) — 208 citations
• Maximum Density Divergence for Domain Adaptation (2020) — 340 citations
• Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks (2019) — 417 citations
• Depth-Induced Multi-Scale Recurrent Attention Network for Saliency Detection (2019) — 468 citations