Josephine Sullivan

Computer Vision and Pattern Recognition · 2013–2014

A researcher in the Science corpus: 7 papers; trusts 780 researchers and is trusted by 5,076 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 197 citations into it; #411 of 21,575 by trust here, #827 by citations
• Artificial Intelligence — 72 citations into it; #14,479 of 35,345 by trust here, #7,945 by citations
• Aerospace Engineering — 28 citations into it; #1,985 of 6,421 by trust here, #3,388 by citations
• Evaluation & Benchmarks — 27 citations into it
• Representation & Self-Supervised Learning — 20 citations into it; #1,580 of 3,145 by trust here, #1,052 by citations
• Information Retrieval & Search — 19 citations into it
• Media Technology — 17 citations into it
• Model Architectures — 9 citations into it

Whom Josephine Sullivan trusts overall

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

1. Andrew Zisserman
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Karen Simonyan
5. Yoshua Bengio
6. Ross B. Girshick
7. Ondřej Chum
8. Radford M. Neal
9. David G. Lowe
10. Kaiming He

Who trusts Josephine Sullivan overall

1. Joon Son Chung
2. Andrew Zisserman
3. Artem Babenko
4. Yu-Xiong Wang
5. Andrea Vedaldi
6. Bolei Zhou
7. Yi Yang
8. Liang Zheng
9. Filip Radenovic
10. Luc Van Gool

Papers from Josephine Sullivan's perspective

Ordered by how strongly the researchers Josephine Sullivan 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. ImageNet: A large-scale hierarchical image database
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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

• CNN Features Off-the-Shelf: An Astounding Baseline for Recognition (2014) — 5,100 citations
• From generic to specific deep representations for visual recognition (2014) — 433 citations
• One millisecond face alignment with an ensemble of regression trees (2014) — 2,798 citations
• Factors of Transferability for a Generic ConvNet Representation (2014) — 355 citations
• 3D Pictorial Structures for Multiple View Articulated Pose Estimation (2013) — 166 citations
• Visual Instance Retrieval with Deep Convolutional Networks (2014) — 383 citations
• Multi-view Body Part Recognition with Random Forests (2013) — 96 citations