84 citations · 392 across the 55 of their papers we have counts for
73 papers
CHOTA: A Higher Order Accuracy Metric for Cell Tracking
Timo Kaiser, Vladimir Ulman, Bodo Rosenhahn
The evaluation of cell tracking results steers the development of tracking methods, significantly impacting biomedical research. This is quantitatively achieved by means of evaluat…
Cell Tracking according to Biological Needs -- Strong Mitosis-aware Multi-Hypothesis Tracker with Aleatoric Uncertainty
Timo Kaiser, Maximilian Schier, Bodo Rosenhahn
Cell tracking and segmentation assist biologists in extracting insights from large-scale microscopy time-lapse data. Driven by local accuracy metrics, current tracking approaches o…
The voraus-AD Dataset for Anomaly Detection in Robot Applications
Jan Thieß Brockmann, Marco Rudolph, Bodo Rosenhahn +1
During the operation of industrial robots, unusual events may endanger the safety of humans and the quality of production. When collecting data to detect such cases, it is not ensu…
HyperSparse Neural Networks: Shifting Exploration to Exploitation through Adaptive Regularization
Patrick Glandorf, Timo Kaiser, Bodo Rosenhahn
Sparse neural networks are a key factor in developing resource-efficient machine learning applications. We propose the novel and powerful sparse learning method Adaptive Regularize…
Markerless human pose estimation for biomedical applications: a survey
Andrea Avogaro, Federico Cunico, Bodo Rosenhahn +1
Markerless Human Pose Estimation (HPE) proved its potential to support decision making and assessment in many fields of application. HPE is often preferred to traditional marker-ba…
Monte Carlo Graph Search for Quantum Circuit Optimization
Bodo Rosenhahn, Tobias J. Osborne
The building blocks of quantum algorithms and software are quantum gates, with the appropriate combination of quantum gates leading to a desired quantum circuit. Deep expert knowle…
Color-aware Deep Temporal Backdrop Duplex Matting System
Hendrik Hachmann, Bodo Rosenhahn
Deep learning-based alpha matting showed tremendous improvements in recent years, yet, feature film production studios still rely on classical chroma keying including costly post-p…
Human Spine Motion Capture using Perforated Kinesiology Tape
Hendrik Hachmann, Bodo Rosenhahn
In this work, we present a marker-based multi-view spine tracking method that is specifically adjusted to the requirements for movements in sports. A maximal focus is on the accura…
Compensation Learning in Semantic Segmentation
Timo Kaiser, Christoph Reinders, Bodo Rosenhahn
Label noise and ambiguities between similar classes are challenging problems in developing new models and annotating new data for semantic segmentation. In this paper, we propose C…
SPAN: Learning Similarity between Scene Graphs and Images with Transformers
Yuren Cong, Wentong Liao, Bodo Rosenhahn +1
Learning similarity between scene graphs and images aims to estimate a similarity score given a scene graph and an image. There is currently no research dedicated to this task, alt…
Take 5: Interpretable Image Classification with a Handful of Features
Thomas Norrenbrock, Marco Rudolph, Bodo Rosenhahn
Deep Neural Networks use thousands of mostly incomprehensible features to identify a single class, a decision no human can follow. We propose an interpretable sparse and low dimens…
Attribute-Centric Compositional Text-to-Image Generation
Yuren Cong, Martin Renqiang Min, Li Erran Li +2
Despite the recent impressive breakthroughs in text-to-image generation, generative models have difficulty in capturing the data distribution of underrepresented attribute composit…
Semantic Segmentation for Fully Automated Macrofouling Analysis on Coatings after Field Exposure
Lutz M. K. Krause, Emily Manderfeld, Patricia Gnutt +8
Biofouling is a major challenge for sustainable shipping, filter membranes, heat exchangers, and medical devices. The development of fouling-resistant coatings requires the evaluat…
Blind Knowledge Distillation for Robust Image Classification
Timo Kaiser, Lukas Ehmann, Christoph Reinders +1
Optimizing neural networks with noisy labels is a challenging task, especially if the label set contains real-world noise. Networks tend to generalize to reasonable patterns in the…
SSGVS: Semantic Scene Graph-to-Video Synthesis
Yuren Cong, Jinhui Yi, Bodo Rosenhahn +1
As a natural extension of the image synthesis task, video synthesis has attracted a lot of interest recently. Many image synthesis works utilize class labels or text as guidance. H…
Asymmetric Student-Teacher Networks for Industrial Anomaly Detection
Marco Rudolph, Tom Wehrbein, Bodo Rosenhahn +1
Industrial defect detection is commonly addressed with anomaly detection (AD) methods where no or only incomplete data of potentially occurring defects is available. This work disc…
AdaCC: Cumulative Cost-Sensitive Boosting for Imbalanced Classification
Vasileios Iosifidis, Symeon Papadopoulos, Bodo Rosenhahn +1
Class imbalance poses a major challenge for machine learning as most supervised learning models might exhibit bias towards the majority class and under-perform in the minority clas…
POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning
Frederik Schubert, Carolin Benjamins, Sebastian Döhler +2
The goal of Unsupervised Reinforcement Learning (URL) is to find a reward-agnostic prior policy on a task domain, such that the sample-efficiency on supervised downstream tasks is…
ChimeraMix: Image Classification on Small Datasets via Masked Feature Mixing
Christoph Reinders, Frederik Schubert, Bodo Rosenhahn
Deep convolutional neural networks require large amounts of labeled data samples. For many real-world applications, this is a major limitation which is commonly treated by augmenta…
Contextualize Me -- The Case for Context in Reinforcement Learning
Carolin Benjamins, Theresa Eimer, Frederik Schubert +6
While Reinforcement Learning ( RL) has made great strides towards solving increasingly complicated problems, many algorithms are still brittle to even slight environmental changes.…
RelTR: Relation Transformer for Scene Graph Generation
Yuren Cong, Michael Ying Yang, Bodo Rosenhahn
Different objects in the same scene are more or less related to each other, but only a limited number of these relationships are noteworthy. Inspired by DETR, which excels in objec…
LMGP: Lifted Multicut Meets Geometry Projections for Multi-Camera Multi-Object Tracking
Duy M. H. Nguyen, Roberto Henschel, Bodo Rosenhahn +2
Multi-Camera Multi-Object Tracking is currently drawing attention in the computer vision field due to its superior performance in real-world applications such as video surveillance…
CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning
Carolin Benjamins, Theresa Eimer, Frederik Schubert +4
While Reinforcement Learning has made great strides towards solving ever more complicated tasks, many algorithms are still brittle to even slight changes in their environment. This…
Fully Convolutional Cross-Scale-Flows for Image-based Defect Detection
Marco Rudolph, Tom Wehrbein, Bodo Rosenhahn +1
In industrial manufacturing processes, errors frequently occur at unpredictable times and in unknown manifestations. We tackle the problem of automatic defect detection without req…