Publications
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Probabilistic Sampling of Balanced K-Means using Adiabatic Quantum Computing
Jan-Nico Zaech, Martin Danelljan, Tolga Birdal, Luc Van Gool IEEE Conference on Computer Vision and Pattern Recognition 2024 (CVPR) 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…
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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.