1 code implementation • 29 Nov 2024 • Sönke Tenckhoff, Mario Koddenbrock, Erik Rodner
Our experimental results show clear evidence for a significant time reduction of up to 53% for semi-automatic compared to manual annotation.
no code implementations • 27 Sep 2024 • Ricardo Knauer, Mario Koddenbrock, Raphael Wallsberger, Nicholas M. Brisson, Georg N. Duda, Deborah Falla, David W. Evans, Erik Rodner
Large language models (LLMs) provide powerful means to leverage prior knowledge for predictive modeling when data is limited.
1 code implementation • 3 Sep 2024 • Ricardo Knauer, Marvin Grimm, Erik Rodner
In practice, we are often faced with small-sized tabular data.
no code implementations • 13 May 2024 • Ricardo Knauer, Erik Rodner
Many industry verticals are confronted with small-sized tabular data.
no code implementations • 9 Oct 2023 • Ricardo Knauer, Erik Rodner
A key challenge in machine learning is to design interpretable models that can reduce their inputs to the best subset for making transparent predictions, especially in the clinical domain.
1 code implementation • 7 Oct 2023 • Dennis Ritter, Mike Hemberger, Marc Hönig, Volker Stopp, Erik Rodner, Kristian Hildebrand
In this paper, we systematically analyze unsupervised domain adaptation pipelines for object classification in a challenging industrial setting.
1 code implementation • CVPR 2023 • Simon Reiß, Constantin Seibold, Alexander Freytag, Erik Rodner, Rainer Stiefelhagen
A vast amount of images and pixel-wise annotations allowed our community to build scalable segmentation solutions for natural domains.
no code implementations • 8 Dec 2022 • Dominik Probst, Hasnain Raza, Erik Rodner
Object detection requires substantial labeling effort for learning robust models.
no code implementations • CVPR 2021 • Simon Reiß, Constantin Seibold, Alexander Freytag, Erik Rodner, Rainer Stiefelhagen
Pixel-wise segmentation is one of the most data and annotation hungry tasks in our field.
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.
no code implementations • 23 May 2017 • Talha Qaiser, Abhik Mukherjee, Chaitanya Reddy Pb, Sai Dileep Munugoti, Vamsi Tallam, Tomi Pitkäaho, Taina Lehtimäki, Thomas Naughton, Matt Berseth, Aníbal Pedraza, Ramakrishnan Mukundan, Matthew Smith, Abhir Bhalerao, Erik Rodner, Marcel Simon, Joachim Denzler, Chao-Hui Huang, Gloria Bueno, David Snead, Ian Ellis, Mohammad Ilyas, Nasir Rajpoot
In this paper, we report on a recent automated Her2 scoring contest, held in conjunction with the annual PathSoc meeting held in Nottingham in June 2016, aimed at systematically comparing and advancing the state-of-the-art Artificial Intelligence (AI) based automated methods for Her2 scoring.
2 code implementations • ICCV 2017 • Marcel Simon, Yang Gao, Trevor Darrell, Joachim Denzler, Erik Rodner
In this paper, we generalize average and bilinear pooling to "alpha-pooling", allowing for learning the pooling strategy during training.
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 • 5 Mar 2017 • Marc Aubreville, Christian Knipfer, Nicolai Oetter, Christian Jaremenko, Erik Rodner, Joachim Denzler, Christopher Bohr, Helmut Neumann, Florian Stelzle, Andreas Maier
For this work, CLE image sequences (7894 images) from patients diagnosed with OSCC were obtained from 4 specific locations in the oral cavity, including the OSCC lesion.
no code implementations • 19 Dec 2016 • Christoph Käding, Erik Rodner, Alexander Freytag, Joachim Denzler
The demands on visual recognition systems do not end with the complexity offered by current large-scale image datasets, such as ImageNet.
4 code implementations • 5 Dec 2016 • Marcel Simon, Erik Rodner, Joachim Denzler
Convolutional neural networks (CNN) pre-trained on ImageNet are the backbone of most state-of-the-art approaches.
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 • 21 Oct 2016 • Erik Rodner, Marcel Simon, Robert B. Fisher, Joachim Denzler
In this paper, we study the sensitivity of CNN outputs with respect to image transformations and noise in the area of fine-grained recognition.
no code implementations • 10 Oct 2016 • Manuel Amthor, Erik Rodner, Joachim Denzler
We propose Impatient Deep Neural Networks (DNNs) which deal with dynamic time budgets during application.
no code implementations • 14 Jun 2016 • Clemens-Alexander Brust, Sven Sickert, Marcel Simon, Erik Rodner, Joachim Denzler
Neural networks and especially convolutional neural networks are of great interest in current computer vision research.
no code implementations • 3 Jul 2015 • Erik Rodner, Marcel Simon, Gunnar Brehm, Stephanie Pietsch, J. Wolfgang Wägele, Joachim Denzler
In the following paper, we present and discuss challenging applications for fine-grained visual classification (FGVC): biodiversity and species analysis.
no code implementations • CVPR 2015 • Christoph Kading, Alexander Freytag, Erik Rodner, Paul Bodesheim, Joachim Denzler
In active learning, all categories occurring in collected data are usually assumed to be known in advance and experts should be able to label every requested instance.
no code implementations • ICCV 2015 • Marcel Simon, Erik Rodner
Part models of object categories are essential for challenging recognition tasks, where differences in categories are subtle and only reflected in appearances of small parts of the object.
no code implementations • 23 Feb 2015 • Clemens-Alexander Brust, Sven Sickert, Marcel Simon, Erik Rodner, Joachim Denzler
Classifying single image patches is important in many different applications, such as road detection or scene understanding.
1 code implementation • 12 Nov 2014 • Marcel Simon, Erik Rodner, Joachim Denzler
Current fine-grained classification approaches often rely on a robust localization of object parts to extract localized feature representations suitable for discrimination.
no code implementations • 20 Aug 2014 • Alexander Freytag, Johannes Rühle, Paul Bodesheim, Erik Rodner, Joachim Denzler
To answer this question, we present an in-depth analysis of the effect of local feature quantization on human recognition performance.
no code implementations • 10 Jul 2014 • Björn Barz, Erik Rodner, Joachim Denzler
ARTOS is all about creating, tuning, and applying object detection models with just a few clicks.
no code implementations • CVPR 2014 • Daniel Haase, Erik Rodner, Joachim Denzler
Therefore, we present a transfer learning method that is able to learn from related training data using an instance-weighted transfer technique.
no code implementations • CVPR 2014 • Christoph Goring, Erik Rodner, Alexander Freytag, Joachim Denzler
In the following paper, we present an approach for finegrained recognition based on a new part detection method.
no code implementations • 17 Oct 2013 • Christoph Göring, Alexander Freytag, Erik Rodner, Joachim Denzler
In this paper, we tackle the problem of visual categorization of dog breeds, which is a surprisingly challenging task due to simultaneously present low interclass distances and high intra-class variances.
no code implementations • 20 Aug 2013 • Erik Rodner, Judy Hoffman, Jeff Donahue, Trevor Darrell, Kate Saenko
Images seen during test time are often not from the same distribution as images used for learning.
no code implementations • CVPR 2013 • Paul Bodesheim, Alexander Freytag, Erik Rodner, Michael Kemmler, Joachim Denzler
In contrast to modeling the support of each known class individually, our approach makes use of a projection in a joint subspace where training samples of all known classes have zero intra-class variance.
no code implementations • CVPR 2013 • Jeff Donahue, Judy Hoffman, Erik Rodner, Kate Saenko, Trevor Darrell
Most successful object classification and detection methods rely on classifiers trained on large labeled datasets.
no code implementations • 15 Jan 2013 • Judy Hoffman, Erik Rodner, Jeff Donahue, Trevor Darrell, Kate Saenko
We present an algorithm that learns representations which explicitly compensate for domain mismatch and which can be efficiently realized as linear classifiers.