Publications

Active and Incremental Learning with Weak Supervision.
Clemens-Alexander Brust and Christoph Käding and Joachim Denzler.
Künstliche Intelligenz (KI). 2020. (accepted)
[bibtex] [pdf] [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]

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 Regression Tasks with Expected Model Output Changes.
Christoph Käding and Erik Rodner and Alexander Freytag and Oliver Mothes and Björn Barz and Joachim Denzler.
British Machine Vision Conference (BMVC). 2018.
[bibtex] [pdf] [code] [supplementary] [abstract]

Keeping the Human in the Loop: Towards Automatic Visual Monitoring in Biodiversity Research.
Joachim Denzler and Christoph Käding and Clemens-Alexander Brust.
International Conference on Ecological Informatics (ICEI). Pages 16. 2018.
[bibtex] [abstract]

Abstract: More and more methods in the area of biodiversity research grounds upon new opportunities arising from modern sensing devices that in principle make it possible to continuously record sensor data from the environment. However, these opportunities allow easy recording of huge amount of data, while its evaluation is difficult, if not impossible due to the enormous effort of manual inspection by the researchers. At the same time, we observe impressive results in computer vision and machine learning that are based on two major developments: firstly, the increased performance of hardware together with the advent of powerful graphical processing units applied in scientific computing. Secondly, the huge amount of, in part, annotated image data provided by today's generation of Facebook and Twitter users that are available easily over databases (e.g., Flickr) and/or search engines. However, for biodiversity applications appropriate data bases of annotated images are still missing. In this presentation we discuss already available methods from computer vision and machine learning together with upcoming challenges in automatic monitoring in biodiversity research. We argue that the key element towards success of any automatic method is the possibility to keep the human in the loop - either for correcting errors and improving the system's quality over time, for providing annotation data at moderate effort, or for acceptance and validation reasons. Thus, we summarize already existing techniques from active and life-long learning together with the enormous developments in automatic visual recognition during the past years. In addition, to allow detection of the unexpected such an automatic system must be capable to find anomalies or novel events in the data. We discuss a generic framework for automatic monitoring in biodiversity research which is the result of collaboration between computer scientists and ecologists of the past years. The key ingredients of such a framework are initial, generic classifier, for example, powerful deep learning architectures, active learning to reduce costly annotation effort by experts, fine-grained recognition to differentiate between visually very similar species, and efficient incremental update of the classifier's model over time. For most of these challenges, we present initial solutions in sample applications. The results comprise the automatic evaluation of images from camera traps, attribute estimation for species, as well as monitoring in-situ data in environmental science. Overall, we like to demonstrate the potentials and open issues in bringing together computer scientists and ecologist to open new research directions for either area.

Finding the Unknown: Novelty Detection with Extreme Value Signatures of Deep Neural Activations.
Alexander Schultheiss and Christoph Käding and Alexander Freytag and Joachim Denzler.
German Conference on Pattern Recognition (GCPR). Pages 226-238. 2017.
[bibtex] [pdf] [supplementary] [abstract]

Towards Automated Visual Monitoring of Individual Gorillas in the Wild.
Brust, Clemens-Alexander and Burghardt, Tilo and Groenenberg, Milou and Käding, Christoph and Kühl, Hjalmar and Manguette, Marie and Denzler, Joachim.
ICCV Workshop on Visual Wildlife Monitoring (ICCV-WS). Pages 2820-2830. 2017.
[bibtex] [pdf] [abstract]

Fast Learning and Prediction for Object Detection using Whitened CNN Features.
Björn Barz and Erik Rodner and Christoph Käding and Joachim Denzler.
arXiv preprint arXiv:1704.02930. 2017.
[bibtex] [pdf] [web]

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]

Fine-tuning Deep Neural Networks in Continuous Learning Scenarios.
Christoph Käding and Erik Rodner and Alexander Freytag and Joachim Denzler.
ACCV Workshop on Interpretation and Visualization of Deep Neural Nets (ACCV-WS). 2016.
[bibtex] [pdf] [web] [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]

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]

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]

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]

ERNA - Embedded, Self-Calibrating Robotic-Arm for Gamificated Learning.
Carsten Seeger and Christoph Käding and Christopher Manthey and David Neuhäuser.
Communications, Circuits and Educational Technologies, International Conference on Education and Educational Technologies II (ECS-EET). Pages 19-24. 2014.
[bibtex]

Universal Eye-tracking Based Text Cursor Warping.
Ralf Biedert and Andreas Dengel and Christoph Käding.
Symposium on Eye Tracking Research and Applications (ETRA). Pages 361-364. 2012.
[bibtex]