Search Results for author: Sascha Saralajew

Found 10 papers, 6 papers with code

A Human-Centric Assessment Framework for AI

no code implementations25 May 2022 Sascha Saralajew, Ammar Shaker, Zhao Xu, Kiril Gashteovski, Bhushan Kotnis, Wiem Ben Rim, Jürgen Quittek, Carolin Lawrence

Inspired by the Turing test, we introduce a human-centric assessment framework where a leading domain expert accepts or rejects the solutions of an AI system and another domain expert.

Combining Visual Saliency Methods and Sparse Keypoint Annotations to Providently Detect Vehicles at Night

1 code implementation25 Apr 2022 Lukas Ewecker, Lars Ohnemus, Robin Schwager, Stefan Roos, Tim Brühl, Sascha Saralajew

We show that this approach allows for an automated derivation of different object representations, such as binary maps or bounding boxes so that detection models can be trained on different annotation variants and the problem of providently detecting vehicles at night can be tackled from different perspectives.

object-detection Object Detection

Provident Vehicle Detection at Night for Advanced Driver Assistance Systems

no code implementations23 Jul 2021 Lukas Ewecker, Ebubekir Asan, Lars Ohnemus, Sascha Saralajew

To demonstrate the usefulness of such an algorithm, the proposed algorithm is deployed in a test vehicle to use the detected light artifacts to control the glare-free high beam system proactively.

Autonomous Driving object-detection +1

A Dataset for Provident Vehicle Detection at Night

1 code implementation27 May 2021 Sascha Saralajew, Lars Ohnemus, Lukas Ewecker, Ebubekir Asan, Simon Isele, Stefan Roos

In this paper, we study the problem of how to map this intuitive human behavior to computer vision algorithms to detect oncoming vehicles at night just from the light reflections they cause by their headlights.

object-detection Object Detection

Provident Vehicle Detection at Night: The PVDN Dataset

1 code implementation31 Dec 2020 Lars Ohnemus, Lukas Ewecker, Ebubekir Asan, Stefan Roos, Simon Isele, Jakob Ketterer, Leopold Müller, Sascha Saralajew

As humans, we intuitively assume oncoming vehicles before the vehicles are actually physically visible by detecting light reflections caused by their headlamps.

Classification-by-Components: Probabilistic Modeling of Reasoning over a Set of Components

1 code implementation NeurIPS 2019 Sascha Saralajew, Lars Holdijk, Maike Rees, Ebubekir Asan, Thomas Villmann

The decomposition of objects into generic components combined with the probabilistic reasoning provides by design a clear interpretation of the classification decision process.

Adversarial Attack Classification +1

Robustness of Generalized Learning Vector Quantization Models against Adversarial Attacks

1 code implementation1 Feb 2019 Sascha Saralajew, Lars Holdijk, Maike Rees, Thomas Villmann

The evaluation suggests that both Generalized LVQ and Generalized Tangent LVQ have a high base robustness, on par with the current state-of-the-art in robust neural network methods.


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