Giorgos Tolias

Computer Vision and Pattern Recognition · 2010–2020

A researcher in the Science corpus: 9 papers; trusts 1,421 researchers and is trusted by 626 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 308 citations into it; #1,673 of 21,575 by trust here, #3,323 by citations
• Artificial Intelligence — 102 citations into it
• Aerospace Engineering — 81 citations into it; #211 of 6,421 by trust here, #673 by citations
• Representation & Self-Supervised Learning — 71 citations into it; #889 of 3,145 by trust here, #1,234 by citations
• Information Retrieval & Search — 42 citations into it; #88 of 1,022 by trust here, #178 by citations
• Media Technology — 41 citations into it
• Model Architectures — 17 citations into it
• Signal Processing — 12 citations into it

Whom Giorgos Tolias trusts overall

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

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

Who trusts Giorgos Tolias overall

1. Filip Radenovic
2. Albert Gordo
3. Ahmet Iscen
4. Jérôme Revaud
5. Yannis Kalantidis
6. Yannis Avrithis
7. Mateusz Budnik
8. Tobias Weyand
9. Liang Zheng
10. Mathilde Caron

Papers from Giorgos Tolias's perspective

Ordered by how strongly the researchers Giorgos Tolias 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Gradient-based learning applied to document recognition
8. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Particular object retrieval with integral max-pooling of CNN activations (2015) — 1,023 citations
• Fine-Tuning CNN Image Retrieval with No Human Annotation (2017) — 1,567 citations
• CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples (2016) — 615 citations
• To Aggregate or Not to aggregate: Selective Match Kernels for Image Search (2013) — 232 citations
• Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking (2018) — 459 citations
• Label Propagation for Deep Semi-Supervised Learning (2019) — 731 citations
• Retrieving landmark and non-landmark images from community photo collections (2010) — 120 citations
• Learning and aggregating deep local descriptors for instance-level recognition (2020) — 116 citations
• Mining on Manifolds: Metric Learning Without Labels (2018) — 122 citations