Yannis Avrithis
Computer Vision and Pattern Recognition · 2008–2020
A researcher in the Science corpus: 10 papers; trusts 1,287 researchers and is trusted by 75 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 278 citations into it; #3,479 of 21,575 by trust here, #5,711 by citations
- Artificial Intelligence — 124 citations into it
- Representation & Self-Supervised Learning — 96 citations into it
- Information Retrieval & Search — 51 citations into it; #179 of 1,022 by trust here, #354 by citations
- Aerospace Engineering — 50 citations into it; #1,879 of 6,421 by trust here, #2,921 by citations
- Media Technology — 32 citations into it; #724 of 3,621 by trust here, #1,770 by citations
- Model Architectures — 23 citations into it
- Optimization & Training — 22 citations into it
Whom Yannis Avrithis trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- David G. Lowe
- Kaiming He
- Alex Krizhevsky
- Geoffrey E. Hinton
- Andrew Zisserman
- Herv'e J'egou
- Karen Simonyan
- Diederik P. Kingma
- Ondřej Chum
- Yann LeCun
Who trusts Yannis Avrithis overall
- Tobias Weyand
- Matthijs Douze
- Artem Babenko
- Jingdong Wang
- Jeff Johnson
- Mateusz Budnik
- Bastian Leibe
- Paul Hongsuck Seo
- Victor S. Lempitsky
- Herv'e J'egou
Papers from Yannis Avrithis's perspective
Ordered by how strongly the researchers Yannis Avrithis trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- 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
- Locally Optimized Product Quantization for Approximate Nearest Neighbor Search (2014) — 315 citations
- Dense Classification and Implanting for Few-Shot Learning (2019) — 206 citations
- Label Propagation for Deep Semi-Supervised Learning (2019) — 731 citations
- Web-Scale Image Clustering Revisited (2015) — 35 citations
- Retrieving landmark and non-landmark images from community photo collections (2010) — 120 citations
- Asymmetric metric learning for knowledge transfer (2020) — 42 citations
- Mining on Manifolds: Metric Learning Without Labels (2018) — 122 citations
- Objective Quality Assessment in Free-Viewpoint Video Production (2008) — 385 citations