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Institution

German Research Centre for Artificial Intelligence

NonprofitKaiserslautern, Rheinland-Pfalz, Germany
About: German Research Centre for Artificial Intelligence is a nonprofit organization based out in Kaiserslautern, Rheinland-Pfalz, Germany. It is known for research contribution in the topics: Ontology (information science) & Machine translation. The organization has 1477 authors who have published 3957 publications receiving 75232 citations. The organization is also known as: DFKI & GRCAI.


Papers
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Posted Content
TL;DR: Vision Transformer (ViT) attains excellent results compared to state-of-the-art convolutional networks while requiring substantially fewer computational resources to train.
Abstract: While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention is either applied in conjunction with convolutional networks, or used to replace certain components of convolutional networks while keeping their overall structure in place. We show that this reliance on CNNs is not necessary and a pure transformer applied directly to sequences of image patches can perform very well on image classification tasks. When pre-trained on large amounts of data and transferred to multiple mid-sized or small image recognition benchmarks (ImageNet, CIFAR-100, VTAB, etc.), Vision Transformer (ViT) attains excellent results compared to state-of-the-art convolutional networks while requiring substantially fewer computational resources to train.

12,690 citations

Journal Article
TL;DR: This paper discusses the approach to achieve high throughput for transactional query processing while allowing concurrent analytical queries, and presents its approach to distributed snapshot isolation and optimized two-phase commit protocols.
Abstract: Modern enterprise applications are currently undergoing a complete paradigm shift away from traditional transactional processing to combined analytical and transactional processing. This challenge of combining two opposing query types in a single database management system results in additional requirements for transaction management as well. In this paper, we discuss our approach to achieve high throughput for transactional query processing while allowing concurrent analytical queries. We present our approach to distributed snapshot isolation and optimized two-phase commit protocols.

1,208 citations

Proceedings Article
03 Jul 2018
TL;DR: This paper introduces a new anomaly detection method—Deep Support Vector Data Description—, which is trained on an anomaly detection based objective and shows the effectiveness of the method on MNIST and CIFAR-10 image benchmark datasets as well as on the detection of adversarial examples of GTSRB stop signs.
Abstract: Despite the great advances made by deep learning in many machine learning problems, there is a relative dearth of deep learning approaches for anomaly detection. Those approaches which do exist involve networks trained to perform a task other than anomaly detection, namely generative models or compression, which are in turn adapted for use in anomaly detection; they are not trained on an anomaly detection based objective. In this paper we introduce a new anomaly detection method—Deep Support Vector Data Description—, which is trained on an anomaly detection based objective. The adaptation to the deep regime necessitates that our neural network and training procedure satisfy certain properties, which we demonstrate theoretically. We show the effectiveness of our method on MNIST and CIFAR-10 image benchmark datasets as well as on the detection of adversarial examples of GTSRB stop signs.

1,070 citations

Proceedings ArticleDOI
18 Jun 2012
TL;DR: A new dataset - recorded from 18 activities performed by 9 subjects, wearing 3 IMUs and a HR-monitor - is created and made publicly available, showing the difficulty of the classification tasks and exposes new challenges for physical activity monitoring.
Abstract: This paper addresses the lack of a commonly used, standard dataset and established benchmarking problems for physical activity monitoring. A new dataset -- recorded from 18 activities performed by 9 subjects, wearing 3 IMUs and a HR-monitor -- is created and made publicly available. Moreover, 4 classification problems are benchmarked on the dataset, using a standard data processing chain and 5 different classifiers. The benchmark shows the difficulty of the classification tasks and exposes new challenges for physical activity monitoring.

902 citations

Journal ArticleDOI
TL;DR: An experimental comparison of a large number of different image descriptors for content-based image retrieval is presented and the often used, but very simple, color histogram performs well in the comparison and thus can be recommended as a simple baseline for many applications.
Abstract: An experimental comparison of a large number of different image descriptors for content-based image retrieval is presented. Many of the papers describing new techniques and descriptors for content-based image retrieval describe their newly proposed methods as most appropriate without giving an in-depth comparison with all methods that were proposed earlier. In this paper, we first give an overview of a large variety of features for content-based image retrieval and compare them quantitatively on four different tasks: stock photo retrieval, personal photo collection retrieval, building retrieval, and medical image retrieval. For the experiments, five different, publicly available image databases are used and the retrieval performance of the features is analyzed in detail. This allows for a direct comparison of all features considered in this work and furthermore will allow a comparison of newly proposed features to these in the future. Additionally, the correlation of the features is analyzed, which opens the way for a simple and intuitive method to find an initial set of suitable features for a new task. The article concludes with recommendations which features perform well for what type of data. Interestingly, the often used, but very simple, color histogram performs well in the comparison and thus can be recommended as a simple baseline for many applications.

641 citations


Authors

Showing all 1500 results

NameH-indexPapersCitations
Bernt Schiele13056870032
Franz Baader6233424544
Rolf Backofen5736314394
Bernhard Nebel5627313207
Elisabeth André5555013587
Rolf Drechsler5495013578
Andreas Bulling5423011385
Paul Lukowicz5336311664
Daniel Braun4942610067
Lucia Specia4832210842
Bruce M. McLaren461926322
Hans Hagen463927313
Volker Markl462589114
Christoph H. Lampert4516512401
Antonio Krüger443637554
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202322
202226
2021346
2020331
2019281
2018229