Computer Vision and Machine Learning
This site provides an overview of active research areas and research projects related to computer vision and machine learning algorithms, currently with a strong focus on applications in visual biodiversity monitoring and nature conservation. More details and related publications can be found on the respective project pages. Contact information for the responsible person can be found below as well.
Fine-grained Recognition

In this research area, we are developing methods that are able to automatically distinguish between very similar object categories. The algorithms learn from given pictures and their annotations the locations as well as the characteristics of relevant features. Applications for this research include automatic biodiversity monitoring.
Time frame: since 2014Understanding Deep Learning

Deep neural networks are tremendously successful in many applications, but end-to-end trained networks often result in hard to understand black-box classifiers or predictors. In this Project, we face this problem. We develop methods, to determine whether a feature is relevant to the decision of a deep neural network. Further, we develop methods to influence which features will be considered by a neural network during training. This can help to learn more robust, trustworthy and fair deep neural networks.
Time frame: since 2017Novelty Detection

In many important learning tasks, training examples are often available for only one class. Learning in this scenario is difficult, since training examples from “outside” are not available at all. This problem is known as one-class classification (OCC), novelty detection, and outlier detection, to name just a few. Our work in this area addresses the use of methods for deriving a set of suitable OCC scores and to apply OCC in an incremental learning framework.
Time frame: since 2012Active Learning

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 explicitely 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 “worthyness” of an unlabeled sample and to apply our algorithms to human-in-the-loop recognition systems.
Time frame: since 2012LEPMON: Monitoring Biodiversity of Moths (Lepidoptera) Using Automated Camera Traps and Artificial Intelligence

InsectAI: Using Image-based AI for Insect Monitoring and Conservation

PhenEye: Having an eye on the fingerprint of global change: observing phenology using automated monitoring

AMMOD: Automated Multisensor station for Monitoring Of species Diversity

CamTrapAI: International Workshop Series on Camera Traps, AI, and Ecology
We are organizing annual workshops worldwide to bring together people interested in developing or working with AI algorithms for analyzing image data and video footage recorded by camera traps. More information are provided here.
ELLIS Unit Jena: Part of the European Laboratory for Learning and Intelligent Systems (ELLIS)
We are collaborating with various scientists from different research disciplines and institutes connected via the ELLIS network and the ELLIS Unit Jena. More information are provided here.


Paul Bodesheim |
Dr.-Ing. |
Postdoctoral Researcher and Lecturer |
Email: ✉️ |
Room: 1218 |
Phone: (+49) 3641 9 46410 |