Longlong Jing

Computer Vision and Pattern Recognition · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 1,928 researchers and is trusted by 213 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 260 citations into it; #2,592 of 21,575 by trust here, #5,565 by citations
• Artificial Intelligence — 123 citations into it; #5,837 of 35,345 by trust here, #8,999 by citations
• Representation & Self-Supervised Learning — 109 citations into it; #610 of 3,145 by trust here, #848 by citations
• Generative Models — 30 citations into it
• Evaluation & Benchmarks — 27 citations into it
• Signal Processing — 16 citations into it
• Multimodal Learning — 14 citations into it
• Aerospace Engineering — 12 citations into it

Whom Longlong Jing trusts overall

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

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

Who trusts Longlong Jing overall

1. Matthew Wallingford
2. Jian Liang
3. Tengda Han
4. Jiangpeng He
5. Yuan Yuan
6. Aditya Mogadala
7. Yassine Ouali
8. Han-Jia Ye
9. Feihong Shen
10. Fanyi Xiao

Papers from Longlong Jing's perspective

Ordered by how strongly the researchers Longlong Jing 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. 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. Going deeper with convolutions

Their papers

• Self-Supervised Visual Feature Learning With Deep Neural Networks: A Survey (2019) — 2,029 citations
• Self-supervised Spatiotemporal Feature Learning by Video Geometric Transformations (2018) — 82 citations
• Self-Supervised Spatiotemporal Feature Learning via Video Rotation Prediction. (2018) — 171 citations