Publications

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. 102: pages 875-897. 2022.
[bibtex] [pdf] [web] [abstract]

Abstract: Animal re-identification based on image data, either recorded manually by photographers or automatically with camera traps, is an important task for ecological studies about biodiversity and conservation that can be highly automatized with algorithms from computer vision and machine learning. However, fixed identification models only trained with standard datasets before their application will quickly reach their limits, especially for long-term monitoring with changing environmental conditions, varying visual appearances of individuals over time that differ a lot from those in the training data, and new occurring individuals that have not been observed before. Hence, we believe that active learning with human-in-the-loop and continuous lifelong learning is important to tackle these challenges and to obtain high-performance recognition systems when dealing with huge amounts of additional data that become available during the application. Our general approach with image features from deep neural networks and decoupled decision models can be applied to many different mammalian species and is perfectly suited for continuous improvements of the recognition systems via lifelong learning. In our identification experiments, we consider four different taxa, namely two elephant species: African forest elephants and Asian elephants, as well as two species of great apes: gorillas and chimpanzees. Going beyond classical re-identification, our decoupled approach can also be used for predicting attributes of individuals such as gender or age using classification or regression methods. Although applicable for small datasets of individuals as well, we argue that even better recognition performance will be achieved by improving decision models gradually via lifelong learning to exploit huge datasets and continuous recordings from long-term applications. We highlight that algorithms for deploying lifelong learning in real observational studies exist and are ready for use. Hence, lifelong learning might become a valuable concept that supports practitioners when analyzing large-scale image data during long-term monitoring of mammals.

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

Information-Theoretic Active Learning for Content-Based Image Retrieval
Björn Barz and Christoph Käding and Joachim Denzler.
DAGM German Conference on Pattern Recognition (DAGM-GCPR). Pages 650-666. 2019.
[bibtex] [pdf] [code] [supplementary] [abstract]

Active Learning for Deep Object Detection
Clemens-Alexander Brust and Christoph Käding and Joachim Denzler.
International Conference on Computer Vision Theory and Applications (VISAPP). Pages 181-190. 2019.
[bibtex] [pdf] [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.

You Have To Look More Than Once: Active and Continuous Exploration using YOLO
Clemens-Alexander Brust and Christoph Käding and Joachim Denzler.
CVPR Workshop on Continuous and Open-Set Learning (CVPR-WS). 2017. Extended Abstract + Poster Presentation
[bibtex] [abstract]

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

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

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.
DAGM German Conference on Pattern Recognition (DAGM-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.
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