63 citations · 120 across the 22 of their papers we have counts for
20 papers · 1 filter
Comparative Study of Out-of-the-Box Technology for Automatic Target Detection and Recognition
Alma M. Liezenga, Lotte Nijskens, Henrik R. Baumann +12
Automatic Target Detection and Recognition (ATD/R) is critical for military decision support and (semi-)autonomous operations. Recent advances in object detection and artificial in…
Structured Adversarial Camouflage via Voronoi Diagrams
Jens Bayer, Stefan Becker, David Münch +2
Pixel-wise adversarial patches are computationally heavy and often visually detectable, limiting utility in security-critical systems. We present adversarial Voronoi camouflage tha…
A Causally Grounded Taxonomy for Image Degradation Robustness Evaluation
Stefan Becker, Simon Weiss, Wolfgang Hübner +1
Image degradations can occur during acquisition, processing, and transmission, altering visual appearance and affecting downstream vision tasks. They are studied in several communi…
Self-Aware Object Detection via Degradation Manifolds
Stefan Becker, Simon Weiss, Wolfgang Hübner +1
Object detectors achieve strong performance under nominal imaging conditions but can fail silently when exposed to blur, noise, compression, adverse weather, or resolution changes.…
Higher-Order Adversarial Patches for Real-Time Object Detectors
Jens Bayer, Stefan Becker, David Münch +2
Higher-order adversarial attacks can directly be considered the result of a cat-and-mouse game -- an elaborate action involving constant pursuit, near captures, and repeated escape…
Traversing the Subspace of Adversarial Patches
Jens Bayer, Stefan Becker, David Münch +2
Despite ongoing research on the topic of adversarial examples in deep learning for computer vision, some fundamentals of the nature of these attacks remain unclear. As the manifold…
Network transferability of adversarial patches in real-time object detection
Jens Bayer, Stefan Becker, David Münch +1
Adversarial patches in computer vision can be used, to fool deep neural networks and manipulate their decision-making process. One of the most prominent examples of adversarial pat…
Strike the Balance: On-the-Fly Uncertainty based User Interactions for Long-Term Video Object Segmentation
Stéphane Vujasinović, Stefan Becker, Sebastian Bullinger +3
In this paper, we introduce a variant of video object segmentation (VOS) that bridges interactive and semi-automatic approaches, termed Lazy Video Object Segmentation (ziVOS). In c…
Utilizing dataset affinity prediction in object detection to assess training data
Stefan Becker, Jens Bayer, Ronny Hug +2
Data pooling offers various advantages, such as increasing the sample size, improving generalization, reducing sampling bias, and addressing data sparsity and quality, but it is no…
Eigenpatches -- Adversarial Patches from Principal Components
Jens Bayer, Stefan Becker, David Münch +1
Adversarial patches are still a simple yet powerful white box attack that can be used to fool object detectors by suppressing possible detections. The patches of these so-called ev…
READMem: Robust Embedding Association for a Diverse Memory in Unconstrained Video Object Segmentation
Stéphane Vujasinović, Sebastian Bullinger, Stefan Becker +3
We present READMem (Robust Embedding Association for a Diverse Memory), a modular framework for semi-automatic video object segmentation (sVOS) methods designed to handle unconstra…
Revisiting Click-based Interactive Video Object Segmentation
Stephane Vujasinovic, Sebastian Bullinger, Stefan Becker +3
While current methods for interactive Video Object Segmentation (iVOS) rely on scribble-based interactions to generate precise object masks, we propose a Click-based interactive Vi…
Generating Synthetic Training Data for Deep Learning-Based UAV Trajectory Prediction
Stefan Becker, Ronny Hug, Wolfgang Hübner +2
Deep learning-based models, such as recurrent neural networks (RNNs), have been applied to various sequence learning tasks with great success. Following this, these models are incr…
MissFormer: (In-)attention-based handling of missing observations for trajectory filtering and prediction
Stefan Becker, Ronny Hug, Wolfgang Hübner +2
In applications such as object tracking, time-series data inevitably carry missing observations. Following the success of deep learning-based models for various sequence learning t…
Handling Missing Observations with an RNN-based Prediction-Update Cycle
Stefan Becker, Ronny Hug, Wolfgang Hübner +2
In tasks such as tracking, time-series data inevitably carry missing observations. While traditional tracking approaches can handle missing observations, recurrent neural networks…
Integration of the 3D Environment for UAV Onboard Visual Object Tracking
Stéphane Vujasinović, Stefan Becker, Timo Breuer +3
Single visual object tracking from an unmanned aerial vehicle (UAV) poses fundamental challenges such as object occlusion, small-scale objects, background clutter, and abrupt camer…
Quantifying the Complexity of Standard Benchmarking Datasets for Long-Term Human Trajectory Prediction
Ronny Hug, Stefan Becker, Wolfgang Hübner +1
Methods to quantify the complexity of trajectory datasets are still a missing piece in benchmarking human trajectory prediction models. In order to gain a better understanding of t…
An RNN-based IMM Filter Surrogate
Stefan Becker, Ronny Hug, Wolfgang Hübner +1
The problem of varying dynamics of tracked objects, such as pedestrians, is traditionally tackled with approaches like the Interacting Multiple Model (IMM) filter using a Bayesian…
An Evaluation of Trajectory Prediction Approaches and Notes on the TrajNet Benchmark
Stefan Becker, Ronny Hug, Wolfgang Hübner +1
In recent years, there is a shift from modeling the tracking problem based on Bayesian formulation towards using deep neural networks. Towards this end, in this paper the effective…
Particle-based pedestrian path prediction using LSTM-MDL models
Ronny Hug, Stefan Becker, Wolfgang Hübner +1
Recurrent neural networks are able to learn complex long-term relationships from sequential data and output a pdf over the state space. Therefore, recurrent models are a natural ch…