333 citations · 1.2k across the 45 of their papers we have counts for
8 papers · 1 filter
Towards Automatic Identification of Elephants in the Wild
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 in…
Integrating domain knowledge: using hierarchies to improve deep classifiers
Clemens-Alexander Brust, Joachim Denzler
One of the most prominent problems in machine learning in the age of deep learning is the availability of sufficiently large annotated datasets. For specific domains, e.g. animal s…
Not just a matter of semantics: the relationship between visual similarity and semantic similarity
Clemens-Alexander Brust, Joachim Denzler
Knowledge transfer, zero-shot learning and semantic image retrieval are methods that aim at improving accuracy by utilizing semantic information, e.g. from WordNet. It is assumed t…
Causal Inference in Nonverbal Dyadic Communication with Relevant Interval Selection and Granger Causality
Lea Müller, Maha Shadaydeh, Martin Thümmel +3
Human nonverbal emotional communication in dyadic dialogs is a process of mutual influence and adaptation. Identifying the direction of influence, or cause-effect relation between…
Active Learning for Deep Object Detection
Clemens-Alexander Brust, Christoph Käding, Joachim Denzler
The great success that deep models have achieved in the past is mainly owed to large amounts of labeled training data. However, the acquisition of labeled data for new tasks aside…
Hierarchy-based Image Embeddings for Semantic Image Retrieval
Björn Barz, Joachim Denzler
Deep neural networks trained for classification have been found to learn powerful image representations, which are also often used for other tasks such as comparing images w.r.t. t…
Information-Theoretic Active Learning for Content-Based Image Retrieval
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…
Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly Detection
Björn Barz, Erik Rodner, Yanira Guanche Garcia +1
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.…