Active Learning

Team

Daphne Auer, Julia Böhlke, Niklas Penzel, Paul Bodesheim

Motivation

Although labeled data lies at the very core of most computer vision systems, obtaining labeled data that is useful and reliable is commonly a crucial problem. To reduce the amount of manual labeling, active learning techniques aim at explicitly picking samples that are actually worth being labeled with respect to the problem on hand. In this area of research, we are interested in modeling the “worthiness” of an unlabeled sample and to apply our algorithms to human-in-the-loop recognition systems.

Publications

2022
Pre-trained models are not enough: active and lifelong learning is important for long-term visual monitoring of mammals in biodiversity research. Individual identification and attribute prediction with image features from deep neural networks and decoupled decision models applied to elephants and great apes
Paul Bodesheim and Jan Blunk and Matthias Körschens and Clemens-Alexander Brust and Christoph Käding and Joachim Denzler.
Mammalian Biology. 2022.
[bibtex] [web] [abstract]
2021
Carpe Diem: A Lifelong Learning Tool for Automated Wildlife Surveillance
Clemens-Alexander Brust and Björn Barz and Joachim Denzler.
Computer Science for Biodiversity Workshop (CS4Biodiversity), INFORMATIK 2021. Pages 417-423. 2021.
[bibtex] [pdf]
Self-Supervised Learning from Semantically Imprecise Data
Clemens-Alexander Brust and Björn Barz and Joachim Denzler.
arXiv preprint arXiv:2104.10901. 2021.
[bibtex] [pdf] [abstract]
Investigating the Consistency of Uncertainty Sampling in Deep Active Learning
Niklas Penzel and Christian Reimers and Clemens-Alexander Brust and Joachim Denzler.
DAGM German Conference on Pattern Recognition (DAGM-GCPR). Pages 159-173. 2021.
[bibtex] [pdf] [web] [abstract]
2020
Active and Incremental Learning with Weak Supervision
Clemens-Alexander Brust and Christoph Käding and Joachim Denzler.
Künstliche Intelligenz (KI). 2020.
[bibtex] [pdf] [abstract]
2019
Information-Theoretic Active Learning for Content-Based Image Retrieval
Björn Barz and Christoph Käding and Joachim Denzler.
German Conference on Pattern Recognition (GCPR) 2018. Lecture Notes in Computer Science. Pages 650-666. 2019.
[bibtex] [pdf] [code] [supplementary] [abstract]
Active Learning for Deep Object Detection
Clemens-Alexander Brust and Christoph Käding and Joachim Denzler.
Computer Vision Theory and Applications (VISAPP). Pages 181-190. 2019.
[bibtex] [pdf] [abstract]
2017
You Have To Look More Than Once: Active and Continuous Exploration using YOLO
Clemens-Alexander Brust and Christoph Käding and Joachim Denzler. 2017. Poster presentation and extended abstract
[bibtex] [abstract]
Large-Scale Gaussian Process Inference with Generalized Histogram Intersection Kernels for Visual Recognition Tasks
Erik Rodner and Alexander Freytag and Paul Bodesheim and Björn Fröhlich and Joachim Denzler.
International Journal of Computer Vision (IJCV). 121 (2) : pp. 253-280. 2017.
[bibtex] [pdf] [web] [abstract]
2016
Lifelong Learning for Visual Recognition Systems
Alexander Freytag. 2016. ISBN 9783843929950
[bibtex] [pdf] [web]
Large-scale Active Learning with Approximated Expected Model Output Changes
Christoph Käding and Alexander Freytag and Erik Rodner and Andrea Perino and Joachim Denzler.
German Conference on Pattern Recognition (GCPR). Pages 179-191. 2016.
[bibtex] [pdf] [web] [code] [supplementary] [abstract]
Active and Continuous Exploration with Deep Neural Networks and Expected Model Output Changes
Christoph Käding and Erik Rodner and Alexander Freytag and Joachim Denzler.
NIPS Workshop on Continual Learning and Deep Networks (NIPS-WS). 2016.
[bibtex] [pdf] [web] [abstract]
Watch, Ask, Learn, and Improve: A Lifelong Learning Cycle for Visual Recognition
Christoph Käding and Erik Rodner and Alexander Freytag and Joachim Denzler.
European Symposium on Artificial Neural Networks (ESANN). Pages 381-386. 2016.
[bibtex] [pdf] [code] [presentation] [abstract]
2015
Active Learning and Discovery of Object Categories in the Presence of Unnameable Instances
Christoph Käding and Alexander Freytag and Erik Rodner and Paul Bodesheim and Joachim Denzler.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Pages 4343-4352. 2015.
[bibtex] [pdf] [web] [code] [presentation] [supplementary] [abstract]
2014
Selecting Influential Examples: Active Learning with Expected Model Output Changes
Alexander Freytag and Erik Rodner and Joachim Denzler.
European Conference on Computer Vision (ECCV). Pages 562-577. 2014.
[bibtex] [pdf] [presentation] [supplementary] [abstract]
2013
Labeling examples that matter: Relevance-Based Active Learning with Gaussian Processes
Alexander Freytag and Erik Rodner and Paul Bodesheim and Joachim Denzler.
German Conference on Pattern Recognition (GCPR). Pages 282-291. 2013.
[bibtex] [pdf] [web] [code] [supplementary] [abstract]
2012
Rapid Uncertainty Computation with Gaussian Processes and Histogram Intersection Kernels
Alexander Freytag and Erik Rodner and Paul Bodesheim and Joachim Denzler.
Asian Conference on Computer Vision (ACCV). Pages 511-524. 2012. Best Paper Honorable Mention Award
[bibtex] [pdf] [web] [presentation] [abstract]