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Jedrzej Kozerawski

dblp:230/1272 · DBLP profile ↗
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2ranked-venue papers
2as first author
0since 2021 · last 2020
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 2 first-authorGraphics, computer vision, multimedia, augmented reality and games · 2 · 2 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Transfer learning and domain adaptation · 33% Time series and sequential data · 33% Probabilistic and Bayesian machine learning · 33%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Probabilistic and Bayesian machine learning
decision boundary estimation		 0.312018
CLEAR: Cumulative LEARning for One-Shot One-Class Image Recognition · CVPR 2018
Machine learning › Transfer learning and domain adaptation
few-shot learning		 0.312018
CLEAR: Cumulative LEARning for One-Shot One-Class Image Recognition · CVPR 2018
Machine learning › Time series and sequential data › anomaly detection
one-class classification		 0.312018
CLEAR: Cumulative LEARning for One-Shot One-Class Image Recognition · CVPR 2018

Methods — techniques the papers use, named apart from their topics

transfer learning · 0.3convolutional neural network · 0.3
YearPublicationVenuePosition
2020 BLT: Balancing Long-Tailed Datasets with Adversarially-Perturbed Images
Jedrzej Kozerawski, Victor Fragoso, Nikolaos Karianakis, Gaurav Mittal, Matthew Turk 0001
 ACCV (3)1
2018 CLEAR: Cumulative LEARning for One-Shot One-Class Image Recognition
abstract
This work addresses the novel problem of one-shot one-class classification. The goal is to estimate a classification decision boundary for a novel class based on a single image example. Our method exploits transfer learning to model the transformation from a representation of the input, extracted by a Convolutional Neural Network, to a classification decision boundary. We use a deep neural network to learn this transformation from a large labelled dataset of images and their associated class decision boundaries generated from ImageNet, and then apply the learned decision boundary to classify subsequent query images. We tested our approach on several benchmark datasets and significantly outperformed the baseline methods.
Jedrzej Kozerawski, Matthew Turk 0001
 CVPR1