3 citations · 6 across the 4 of their papers we have counts for
7 papers
Safety Concerns and Mitigation Approaches Regarding the Use of Deep Learning in Safety-Critical Perception Tasks
Oliver Willers, Sebastian Sudholt, Shervin Raafatnia +1
Deep learning methods are widely regarded as indispensable when it comes to designing perception pipelines for autonomous agents such as robots, drones or automated vehicles. The m…
Exploring Architectures for CNN-Based Word Spotting
Eugen Rusakov, Sebastian Sudholt, Fabian Wolf +1
The goal in word spotting is to retrieve parts of document images which are relevant with respect to a certain user-defined query. The recent past has seen attribute-based Convolut…
Weakly Supervised Object Detection with Pointwise Mutual Information
Rene Grzeszick, Sebastian Sudholt, Gernot A. Fink
In this work a novel approach for weakly supervised object detection that incorporates pointwise mutual information is presented. A fully convolutional neural network architecture…
Learning Deep Representations for Word Spotting Under Weak Supervision
Neha Gurjar, Sebastian Sudholt, Gernot A. Fink
Convolutional Neural Networks have made their mark in various fields of computer vision in recent years. They have achieved state-of-the-art performance in the field of document an…
Attribute CNNs for Word Spotting in Handwritten Documents
Sebastian Sudholt, Gernot Fink
Word spotting has become a field of strong research interest in document image analysis over the last years. Recently, AttributeSVMs were proposed which predict a binary attribute…
Optimistic and Pessimistic Neural Networks for Scene and Object Recognition
Rene Grzeszick, Sebastian Sudholt, Gernot A. Fink
In this paper the application of uncertainty modeling to convolutional neural networks is evaluated. A novel method for adjusting the network's predictions based on uncertainty inf…
PHOCNet: A Deep Convolutional Neural Network for Word Spotting in Handwritten Documents
Sebastian Sudholt, Gernot A. Fink
In recent years, deep convolutional neural networks have achieved state of the art performance in various computer vision task such as classification, detection or segmentation. Du…