Jack Sim

Computer Vision and Pattern Recognition · 2016–2020

A researcher in the Science corpus: 5 papers; trusts 854 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 169 citations into it; #7,799 of 21,575 by trust here, #6,371 by citations
• Aerospace Engineering — 67 citations into it
• Artificial Intelligence — 31 citations into it; #26,908 of 35,345 by trust here, #14,718 by citations
• Information Retrieval & Search — 28 citations into it
• Media Technology — 22 citations into it
• Model Architectures — 15 citations into it
• Evaluation & Benchmarks — 12 citations into it
• Representation & Self-Supervised Learning — 9 citations into it

Whom Jack Sim trusts overall

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

1. David G. Lowe
2. Kaiming He
3. Herv'e J'egou
4. Andrew Zisserman
5. Geoffrey E. Hinton
6. Alex Krizhevsky
7. Ondřej Chum
8. Ross B. Girshick
9. Christian Szegedy
10. Yoshua Bengio

Who trusts Jack Sim overall

1. Bingyi Cao
2. Giorgos Tolias
3. Andre F. de Araújo
4. Shiv Ram Dubey
5. Tobias Weyand
6. Filip Radenovic

Papers from Jack Sim's perspective

Ordered by how strongly the researchers Jack Sim 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Large-Scale Image Retrieval with Attentive Deep Local Features (2016) — 860 citations
• Google Landmarks Dataset v2 – A Large-Scale Benchmark for Instance-Level Recognition and Retrieval (2020) — 477 citations
• Computing Receptive Fields of Convolutional Neural Networks (2019) — 323 citations
• Unifying Deep Local and Global Features for Image Search (2020) — 396 citations
• CPlaNet: Enhancing Image Geolocalization by Combinatorial Partitioning of Maps (2018) — 112 citations