no code implementations • EMNLP (LaTeCHCLfL, CLFL, LaTeCH) 2021 • Felix Schneider, Björn Barz, Phillip Brandes, Sophie Marshall, Joachim Denzler
In contrast, we propose an approach targeting the more general and challenging case A B B’ A’, where the words A, A’ and B, B’ constituting the chiasmus do not need to be identical but just related in meaning.
1 code implementation • IEEE Access 2022 • Lorenzo Brigato, Björn Barz, Luca Iocchi, Joachim Denzler
However, as research in this scope is still in its infancy, two key ingredients are missing for ensuring reliable and truthful progress: a systematic and extensive overview of the state of the art, and a common benchmark to allow for objective comparisons between published methods.
no code implementations • 22 Oct 2021 • Bernd Gruner, Matthias Körschens, Björn Barz, Joachim Denzler
We discovered that domain adaptation works very well for fine-grained recognition and that the normalization methods have a great influence on the results.
no code implementations • 28 Sep 2021 • Björn Barz, Lorenzo Brigato, Luca Iocchi, Joachim Denzler
Learning from limited amounts of data is the hallmark of intelligence, requiring strong generalization and abstraction skills.
no code implementations • 14 Sep 2021 • Violeta Teodora Trifunov, Maha Shadaydeh, Björn Barz, Joachim Denzler
There are numerous methods for detecting anomalies in time series, but that is only the first step to understanding them.
1 code implementation • 30 Aug 2021 • Lorenzo Brigato, Björn Barz, Luca Iocchi, Joachim Denzler
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 past.
no code implementations • 16 Aug 2021 • Björn Barz, Joachim Denzler
We introduce a novel dataset for architectural style classification, consisting of 9, 485 images of church buildings.
no code implementations • 22 Apr 2021 • Clemens-Alexander Brust, Björn Barz, Joachim Denzler
Learning from imprecise labels such as "animal" or "bird", but making precise predictions like "snow bunting" at inference time is an important capability for any classifier when expertly labeled training data is scarce.
no code implementations • 12 Nov 2020 • 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 image.
1 code implementation • 11 Nov 2020 • Björn Barz, Kai Schröter, Ann-Christin Kra, Joachim Denzler
The analysis of natural disasters such as floods in a timely manner often suffers from limited data due to coarsely distributed sensors or sensor failures.
no code implementations • 13 Oct 2020 • Clemens-Alexander Brust, Björn Barz, Joachim Denzler
For example, a non-breeding snow bunting is labeled as a bird.
1 code implementation • 9 Aug 2019 • Björn Barz, Kai Schröter, Moritz Münch, Bin Yang, Andrea Unger, Doris Dransch, Joachim Denzler
The analysis of natural disasters such as floods in a timely manner often suffers from limited data due to a coarse distribution of sensors or sensor failures.
no code implementations • 1 Feb 2019 • Björn Barz, Joachim Denzler
However, we find that 3. 3% and 10% of the images from the test sets of these datasets have duplicates in the training set.
1 code implementation • 25 Jan 2019 • Björn Barz, Joachim Denzler
The categorical cross-entropy loss after softmax activation is the method of choice for classification.
no code implementations • 11 Dec 2018 • Matthias Körschens, Björn Barz, Joachim Denzler
Identifying animals from a large group of possible individuals is very important for biodiversity monitoring and especially for collecting data on a small number of particularly interesting individuals, as these have to be identified first before this can be done.
1 code implementation • 26 Sep 2018 • Björn Barz, Joachim Denzler
Such an embedding does not only improve image retrieval results, but could also facilitate integrating semantics for other tasks, e. g., novelty detection or few-shot learning.
1 code implementation • 7 Sep 2018 • Björn Barz, Christoph Käding, Joachim Denzler
We propose Information-Theoretic Active Learning (ITAL), a novel batch-mode active learning method for binary classification, and apply it for acquiring meaningful user feedback in the context of content-based image retrieval.
1 code implementation • 19 Apr 2018 • Björn Barz, Erik Rodner, Yanira Guanche Garcia, Joachim Denzler
Automatic detection of anomalies in space- and time-varying measurements is an important tool in several fields, e. g., fraud detection, climate analysis, or healthcare monitoring.
1 code implementation • 2 Nov 2017 • Björn Barz, Joachim Denzler
Query images presented to content-based image retrieval systems often have various different interpretations, making it difficult to identify the search objective pursued by the user.
no code implementations • 10 Apr 2017 • Björn Barz, Erik Rodner, Christoph Käding, Joachim Denzler
We combine features extracted from pre-trained convolutional neural networks (CNNs) with the fast, linear Exemplar-LDA classifier to get the advantages of both: the high detection performance of CNNs, automatic feature engineering, fast model learning from few training samples and efficient sliding-window detection.
no code implementations • 21 Oct 2016 • Erik Rodner, Björn Barz, Yanira Guanche, Milan Flach, Miguel Mahecha, Paul Bodesheim, Markus Reichstein, Joachim Denzler
We present new methods for batch anomaly detection in multivariate time series.
no code implementations • 10 Jul 2014 • Björn Barz, Erik Rodner, Joachim Denzler
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