Yuliya Tarabalka

Computer Vision and Pattern Recognition · 2009–2019

A researcher in the Science corpus: 8 papers; trusts 270 researchers and is trusted by 39 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 79 citations into it; #7,914 of 21,575 by trust here, #4,469 by citations
• Media Technology — 53 citations into it; #322 of 3,621 by trust here, #170 by citations
• Artificial Intelligence — 52 citations into it; #30,913 of 35,345 by trust here, #19,439 by citations
• Atmospheric Science — 29 citations into it; #237 of 4,804 by trust here, #452 by citations
• Analytical Chemistry — 20 citations into it
• Representation & Self-Supervised Learning — 16 citations into it
• Meta & Continual Learning — 11 citations into it; #951 of 1,041 by trust here, #711 by citations
• Model Architectures — 10 citations into it

Whom Yuliya Tarabalka trusts overall

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

1. David A. Landgrebe
2. Gustau Camps-Valls
3. Jon Atli Benediktsson
4. Geoffrey E. Hinton
5. Chih-Jen Lin
6. Martino Pesaresi
7. Karen Simonyan
8. Vladimir Naumovich Vapnik
9. Yoshua Bengio
10. Alex Krizhevsky

Who trusts Yuliya Tarabalka overall

1. Shutao Li
2. Lin He
3. Xudong Kang
4. Leyuan Fang
5. Jon Atli Benediktsson
6. E. Aptoula
7. Jun Yu Li
8. Nicolas Audebert
9. Bing Liu
10. Devis Tuia

Papers from Yuliya Tarabalka's perspective

Ordered by how strongly the researchers Yuliya Tarabalka 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. Gradient-based learning applied to document recognition
5. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Can semantic labeling methods generalize to any city? the inria aerial image labeling benchmark (2017) — 811 citations
• Incremental Learning for Semantic Segmentation of Large-Scale Remote Sensing Data (2018) — 144 citations
• Segmentation and classification of hyperspectral images using watershed transformation (2010) — 589 citations
• Continual Learning for Dense Labeling of Satellite Images (2019) — 7 citations
• Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification (2017) — 990 citations
• Advances in Spectral-Spatial Classification of Hyperspectral Images (2013) — 1,259 citations
• SVM- and MRF-Based Method for Accurate Classification of Hyperspectral Images (2010) — 730 citations
• Spectral–Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques (2009) — 754 citations