James Philbin

Computer Vision and Pattern Recognition · 2007–2016

A researcher in the Science corpus: 9 papers; trusts 954 researchers and is trusted by 8,344 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 206 citations into it; #232 of 21,575 by trust here, #378 by citations
• Artificial Intelligence — 69 citations into it; #12,798 of 35,345 by trust here, #3,998 by citations
• Aerospace Engineering — 49 citations into it; #2,087 of 6,421 by trust here, #1,983 by citations
• Evaluation & Benchmarks — 23 citations into it
• Optimization & Training — 20 citations into it
• Media Technology — 19 citations into it
• Model Architectures — 19 citations into it
• Representation & Self-Supervised Learning — 15 citations into it; #1,669 of 3,145 by trust here, #794 by citations

Whom James Philbin trusts overall

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

1. David G. Lowe
2. Josef Sivic
3. Andrew Zisserman
4. Geoffrey E. Hinton
5. David Nistér
6. Alex Krizhevsky
7. Radford M. Neal
8. Yann LeCun
9. Cordelia Schmid
10. Yoshua Bengio

Who trusts James Philbin overall

1. Ondřej Chum
2. Herv'e J'egou
3. Andrew Zisserman
4. Giorgos Tolias
5. Liang Zheng
6. Matthijs Douze
7. Albert Gordo
8. Filip Radenovic
9. Relja Arandjelovi'c
10. Cordelia Schmid

Papers from James Philbin's perspective

Ordered by how strongly the researchers James Philbin 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. 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

• FaceNet: A unified embedding for face recognition and clustering (2015) — 15,122 citations
• Object retrieval with large vocabularies and fast spatial matching (2007) — 3,312 citations
• Learning Fine-Grained Image Similarity with Deep Ranking (2014) — 1,369 citations
• Total Recall: Automatic Query Expansion with a Generative Feature Model for Object Retrieval (2007) — 986 citations
• Lost in quantization: Improving particular object retrieval in large scale image databases (2008) — 1,690 citations
• Deep Stereo: Learning to Predict New Views from the World's Imagery (2015) — 702 citations
• The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition (2015) — 374 citations
• PlaNet - Photo Geolocation with Convolutional Neural Networks (2016) — 494 citations
• Near Duplicate Image Detection: min-Hash and tf-idf Weighting (2008) — 522 citations