31 citations · 35 across the 6 of their papers we have counts for
16 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…
Anomaly Attribution of Multivariate Time Series using Counterfactual Reasoning
Violeta Teodora Trifunov, Maha Shadaydeh, Björn Barz +1
There are numerous methods for detecting anomalies in time series, but that is only the first step to understanding them. We strive to exceed this by explaining those anomalies. Th…
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…
Content-based Image Retrieval and the Semantic Gap in the Deep Learning Era
Björn Barz, Joachim Denzler
Content-based image retrieval has seen astonishing progress over the past decade, especially for the task of retrieving images of the same object that is depicted in the query imag…