Publications
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Probabilistic Sampling of Balanced K-Means using Adiabatic Quantum Computing
Jan-Nico Zaech, Martin Danelljan, Luc Van Gool arXiv 2023 Abstract Adiabatic quantum computing (AQC) is a promising approach for discrete and often NP-hard optimization problems. Current AQCs allow to implement problems of research interest, which has sparked the development of quantum representations for many computer vision tasks. Despite requiring multiple measurements from the noisy AQC,…
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Optimizing Long-Term Player Tracking and Identification in NAO Robot Soccer by fusing Game-state and External Video
A collaborative sensing approach for multi object tracking of robots.
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Adiabatic Quantum Computing for Multi Object Tracking
A Multi-Object Tracking algorithm that can be solved with Adiabatic Quantum Computing
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Learnable Online Graph Representations for 3D Multi-Object Tracking
An online 3D Multi-Object Tracking method based on graph neural networks.
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Unsupervised robust domain adaptation without source data
A method that keeps a network robust against adversarial images during source-free domain adaptation.
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Decoder fusion RNN: Context and interaction aware decoders for trajectory prediction
A multi-headed attention based method for vehicle trajectory prediction using map data encoded on a graph.
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Action sequence predictions of vehicles in urban environments using map and social context
Views the traffic agent trajectory prediction task form a classification perspective and proposes a method to automatically annotate trajectory data by using graph-based maps.
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Texture Underfitting for Domain Adaptation
A method to use image structure in domain adaptation implemented as a two stage training process.
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Learning to avoid poor images: Towards task-aware C-arm cone-beam CT trajectories
A robotic CBCT system that that predicts an acquisition trajectory optimized online during a scan.