Publications

Determining the Community Composition of Herbaceous Species from Images using Convolutional Neural Networks
Matthias Körschens and Solveig Franziska Bucher and Paul Bodesheim and Josephine Ulrich and Joachim Denzler and Christine Römermann.
Ecological Informatics. 80: pages 102516. 2024.
[bibtex] [pdf] [web] [abstract]

Unified Automatic Plant Cover and Phenology Prediction
Matthias Körschens and Solveig Franziska Bucher and Christine Römermann and Joachim Denzler.
ICCV Workshop on Computer Vision in Plant Phenotyping and Agriculture (CVPPA). 2023.
[bibtex] [pdf] [abstract]

Improving Data Efficiency for Plant Cover Prediction with Label Interpolation and Monte-Carlo Cropping
Matthias Körschens and Solveig Franziska Bucher and Christine Römermann and Joachim Denzler.
DAGM German Conference on Pattern Recognition (DAGM-GCPR). 2023.
[bibtex] [pdf] [web] [supplementary] [abstract]

Occlusion-Robustness of Convolutional Neural Networks via Inverted Cutout
Matthias Körschens and Paul Bodesheim and Joachim Denzler.
International Conference on Pattern Recognition (ICPR). Pages 2829-2835. 2022.
[bibtex] [pdf] [supplementary] [abstract]

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.

Beyond Global Average Pooling: Alternative Feature Aggregations for Weakly Supervised Localization
Matthias Körschens and Paul Bodesheim and Joachim Denzler.
International Conference on Computer Vision Theory and Applications (VISAPP). Pages 180-191. 2022.
[bibtex] [pdf] [abstract]

Automatic Plant Cover Estimation with Convolutional Neural Networks
Matthias Körschens and Paul Bodesheim and Christine Römermann and Solveig Franziska Bucher and Mirco Migliavacca and Josephine Ulrich and Joachim Denzler.
Computer Science for Biodiversity Workshop (CS4Biodiversity), INFORMATIK 2021. Pages 499-516. 2021.
[bibtex] [pdf] [abstract]

Weakly Supervised Segmentation Pretraining for Plant Cover Prediction
Matthias Körschens and Paul Bodesheim and Christine Römermann and Solveig Franziska Bucher and Mirco Migliavacca and Josephine Ulrich and Joachim Denzler.
DAGM German Conference on Pattern Recognition (DAGM-GCPR). Pages 589-603. 2021.
[bibtex] [pdf] [supplementary] [abstract]

Domain Adaptation and Active Learning for Fine-Grained Recognition in the Field of Biodiversity
Bernd Gruner and Matthias Körschens and Björn Barz and Joachim Denzler.
Findings of the CVPR Workshop on Continual Learning in Computer Vision (CLVision). 2021.
[bibtex] [abstract]

Towards Confirmable Automated Plant Cover Determination
Matthias Körschens and Paul Bodesheim and Christine Römermann and Solveig Franziska Bucher and Josephine Ulrich and Joachim Denzler.
ECCV Workshop on Computer Vision Problems in Plant Phenotyping (CVPPP). 2020.
[bibtex] [pdf] [web] [supplementary] [abstract]

ELPephants: A Fine-Grained Dataset for Elephant Re-Identification
Matthias Körschens and Joachim Denzler.
ICCV Workshop on Computer Vision for Wildlife Conservation (ICCV-WS). 2019.
[bibtex] [pdf] [abstract]

Towards Automatic Identification of Elephants in the Wild
Matthias Körschens and Björn Barz and Joachim Denzler.
AI for Wildlife Conservation Workshop (AIWC). 2018.
[bibtex] [pdf] [abstract]