33 citations · 142 across the 14 of their papers we have counts for
34 papers
Domain Adaptation and Active Learning for Fine-Grained Recognition in the Field of Biodiversity
Bernd Gruner, Matthias Körschens, Björn Barz +1
Deep-learning methods offer unsurpassed recognition performance in a wide range of domains, including fine-grained recognition tasks. However, in most problem areas there are insuf…
A Strong Baseline for the VIPriors Data-Efficient Image Classification Challenge
Björn Barz, Lorenzo Brigato, Luca Iocchi +1
Learning from limited amounts of data is the hallmark of intelligence, requiring strong generalization and abstraction skills. In a machine learning context, data-efficient methods…
Tune It or Don't Use It: Benchmarking Data-Efficient Image Classification
Lorenzo Brigato, Björn Barz, Luca Iocchi +1
Data-efficient image classification using deep neural networks in settings, where only small amounts of labeled data are available, has been an active research area in the recent p…
WikiChurches: A Fine-Grained Dataset of Architectural Styles with Real-World Challenges
Björn Barz, Joachim Denzler
We introduce a novel dataset for architectural style classification, consisting of 9,485 images of church buildings. Both images and style labels were sourced from Wikipedia. The d…
Automatic Plant Cover Estimation with Convolutional Neural Networks
Matthias Körschens, Paul Bodesheim, Christine Römermann +4
Monitoring the responses of plants to environmental changes is essential for plant biodiversity research. This, however, is currently still being done manually by botanists in the…
Towards Learning an Unbiased Classifier from Biased Data via Conditional Adversarial Debiasing
Christian Reimers, Paul Bodesheim, Jakob Runge +1
Bias in classifiers is a severe issue of modern deep learning methods, especially for their application in safety- and security-critical areas. Often, the bias of a classifier is a…