Till Quack

Computer Vision and Pattern Recognition · 2006–2012

A researcher in the Science corpus: 7 papers; trusts 972 researchers and is trusted by 475 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 134 citations into it; #1,919 of 21,575 by trust here, #4,887 by citations
• Artificial Intelligence — 33 citations into it
• Information Systems — 25 citations into it; #3,243 of 8,771 by trust here, #5,078 by citations
• Information Retrieval & Search — 21 citations into it; #705 of 1,022 by trust here, #989 by citations
• Aerospace Engineering — 20 citations into it; #4,105 of 6,421 by trust here, #3,519 by citations
• Media Technology — 18 citations into it
• Computational Theory and Mathematics — 11 citations into it
• Signal Processing — 11 citations into it; #3,301 of 7,572 by trust here, #4,373 by citations

Whom Till Quack trusts overall

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

1. David G. Lowe
2. Christian Borgelt
3. Andrew Zisserman
4. Josef Sivic
5. Bastian Leibe
6. Rob Fergus
7. J. Ross Quinlan
8. Cordelia Schmid
9. David Nistér
10. Krystian Mikolajczyk

Who trusts Till Quack overall

1. Tobias Weyand
2. Yong Jae Lee
3. Bastian Leibe
4. Yannis Avrithis
5. Kristen Grauman
6. Hyo Jin Kim
7. Charu C. Aggarwal
8. Torsten Sattler
9. Paul Hongsuck Seo
10. Lorenzo Torresani

Papers from Till Quack's perspective

Ordered by how strongly the researchers Till Quack 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. 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

• Proceedings of the 5th International Conference on Image and Video Retrieval (2006) — 630 citations
• Apparel Classification with Style (2012) — 252 citations
• Efficient Mining of Frequent and Distinctive Feature Configurations (2007) — 196 citations
• World-scale mining of objects and events from community photo collections (2008) — 296 citations
• I know what you did last summer: object-level auto-annotation of holiday snaps (2009) — 102 citations
• Hello neighbor: Accurate object retrieval with k-reciprocal nearest neighbors (2011) — 344 citations
• Large scale mining and retrieval of visual data in a multimodal context (2008) — 6 citations