333 citations · 1.2k across the 45 of their papers we have counts for
53 papers
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…
Simplified Concrete Dropout -- Improving the Generation of Attribution Masks for Fine-grained Classification
Dimitri Korsch, Maha Shadaydeh, Joachim Denzler
Fine-grained classification is a particular case of a classification problem, aiming to classify objects that share the visual appearance and can only be distinguished by subtle di…
Improving Data Efficiency for Plant Cover Prediction with Label Interpolation and Monte-Carlo Cropping
Matthias Körschens, Solveig Franziska Bucher, Christine Römermann +1
The plant community composition is an essential indicator of environmental changes and is, for this reason, usually analyzed in ecological field studies in terms of the so-called p…
Image Classification with Small Datasets: Overview and Benchmark
L. Brigato, B. Barz, L. Iocchi +1
Image classification with small datasets has been an active research area in the recent past. However, as research in this scope is still in its infancy, two key ingredients are mi…
Domain Adaptation and Active Learning for Fine-Grained Recognition in the Field of Biodiversity
Bernd Gruner, Matthias Körschens, Björn Barz +1
Deep-learning methods offer unsurpassed recognition performance in a wide range of domains, including fine-grained recognition tasks. However, in most problem areas there are insuf…
A Strong Baseline for the VIPriors Data-Efficient Image Classification Challenge
Björn Barz, Lorenzo Brigato, Luca Iocchi +1
Learning from limited amounts of data is the hallmark of intelligence, requiring strong generalization and abstraction skills. In a machine learning context, data-efficient methods…
Tune It or Don't Use It: Benchmarking Data-Efficient Image Classification
Lorenzo Brigato, Björn Barz, Luca Iocchi +1
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 p…
WikiChurches: A Fine-Grained Dataset of Architectural Styles with Real-World Challenges
Björn Barz, Joachim Denzler
We introduce a novel dataset for architectural style classification, consisting of 9,485 images of church buildings. Both images and style labels were sourced from Wikipedia. The d…
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…
Counterfactual Generation with Knockoffs
Oana-Iuliana Popescu, Maha Shadaydeh, Joachim Denzler
Human interpretability of deep neural networks' decisions is crucial, especially in domains where these directly affect human lives. Counterfactual explanations of already trained…
Content-based Image Retrieval and the Semantic Gap in the Deep Learning Era
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 imag…
Finding Relevant Flood Images on Twitter using Content-based Filters
Björn Barz, Kai Schröter, Ann-Christin Kra +1
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. At the same time, a plet…
Making Every Label Count: Handling Semantic Imprecision by Integrating Domain Knowledge
Clemens-Alexander Brust, Björn Barz, Joachim Denzler
Noisy data, crawled from the web or supplied by volunteers such as Mechanical Turkers or citizen scientists, is considered an alternative to professionally labeled data. There has…
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…
Single-Shot 3D Detection of Vehicles from Monocular RGB Images via Geometry Constrained Keypoints in Real-Time
Nils Gählert, Jun-Jun Wan, Nicolas Jourdan +3
In this paper we propose a novel 3D single-shot object detection method for detecting vehicles in monocular RGB images. Our approach lifts 2D detections to 3D space by predicting a…
Visibility Guided NMS: Efficient Boosting of Amodal Object Detection in Crowded Traffic Scenes
Nils Gählert, Niklas Hanselmann, Uwe Franke +1
Object detection is an important task in environment perception for autonomous driving. Modern 2D object detection frameworks such as Yolo, SSD or Faster R-CNN predict multiple bou…
Cityscapes 3D: Dataset and Benchmark for 9 DoF Vehicle Detection
Nils Gählert, Nicolas Jourdan, Marius Cordts +2
Detecting vehicles and representing their position and orientation in the three dimensional space is a key technology for autonomous driving. Recently, methods for 3D vehicle detec…
Sensor Artificial Intelligence and its Application to Space Systems -- A White Paper
Anko Börner, Heinz-Wilhelm Hübers, Odej Kao +15
Information and communication technologies have accompanied our everyday life for years. A steadily increasing number of computers, cameras, mobile devices, etc. generate more and…
LOST: A flexible framework for semi-automatic image annotation
Jonas Jäger, Gereon Reus, Joachim Denzler +2
State-of-the-art computer vision approaches rely on huge amounts of annotated data. The collection of such data is a time consuming process since it is mainly performed by humans.…
Facial Behavior Analysis using 4D Curvature Statistics for Presentation Attack Detection
Martin Thümmel, Sven Sickert, Joachim Denzler
The human face has a high potential for biometric identification due to its many individual traits. At the same time, such identification is vulnerable to biometric copies. These p…
Predicting Landscapes from Environmental Conditions Using Generative Networks
Christian Requena-Mesa, Markus Reichstein, Miguel Mahecha +2
Landscapes are meaningful ecological units that strongly depend on the environmental conditions. Such dependencies between landscapes and the environment have been noted since the…
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…