36 citations · 63 across the 9 of their papers we have counts for
10 papers
Saccadic Vision for Fine-Grained Visual Classification
Johann Schmidt, Sebastian Stober, Joachim Denzler +1
Fine-grained visual classification (FGVC) requires distinguishing between visually similar categories through subtle, localized features - a task that remains challenging due to hi…
Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models
Aishwarya Venkataramanan, Paul Bodesheim, Joachim Denzler
Vision-Language Models (VLMs) learn joint representations by mapping images and text into a shared latent space. However, recent research highlights that deterministic embeddings f…
Automated Visual Monitoring of Nocturnal Insects with Light-based Camera Traps
Dimitri Korsch, Paul Bodesheim, Gunnar Brehm +1
Automatic camera-assisted monitoring of insects for abundance estimations is crucial to understand and counteract ongoing insect decline. In this paper, we present two datasets of…
Deep Learning Pipeline for Automated Visual Moth Monitoring: Insect Localization and Species Classification
Dimitri Korsch, Paul Bodesheim, Joachim Denzler
Biodiversity monitoring is crucial for tracking and counteracting adverse trends in population fluctuations. However, automatic recognition systems are rarely applied so far, and e…
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…
End-to-end Learning of a Fisher Vector Encoding for Part Features in Fine-grained Recognition
Dimitri Korsch, Paul Bodesheim, Joachim Denzler
Part-based approaches for fine-grained recognition do not show the expected performance gain over global methods, although explicitly focusing on small details that are relevant fo…
Classification-Specific Parts for Improving Fine-Grained Visual Categorization
Dimitri Korsch, Paul Bodesheim, Joachim Denzler
Fine-grained visual categorization is a classification task for distinguishing categories with high intra-class and small inter-class variance. While global approaches aim at using…
Maximally Divergent Intervals for Anomaly Detection
Erik Rodner, Björn Barz, Yanira Guanche +5
We present new methods for batch anomaly detection in multivariate time series. Our methods are based on maximizing the Kullback-Leibler divergence between the data distribution wi…
Seeing through bag-of-visual-word glasses: towards understanding quantization effects in feature extraction methods
Alexander Freytag, Johannes Rühle, Paul Bodesheim +2
Vector-quantized local features frequently used in bag-of-visual-words approaches are the backbone of popular visual recognition systems due to both their simplicity and their perf…