Ehm, ViktoriaCremers, DanielBernard, FlorianSingh, GurpritChu, Mengyu (Rachel)2023-05-032023-05-032023978-3-03868-211-01017-4656https://doi.org/10.2312/egp.20231028https://diglib.eg.org:443/handle/10.2312/egp20231028Flows in networks (or graphs) play a significant role in numerous computer vision tasks. The scalar-valued edges in these graphs often lead to a loss of information and thereby to limitations in terms of expressiveness. For example, oftentimes highdimensional data (e.g. feature descriptors) are mapped to a single scalar value (e.g. the similarity between two feature descriptors). To overcome this limitation, we propose a novel formalism for non-separable multi-dimensional network flows. By doing so, we enable an automatic and adaptive feature selection strategy - since the flow is defined on a per-dimension basis, the maximizing flow automatically chooses the best matching feature dimensions. As a proof of concept, we apply our formalism to the multi-object tracking problem and demonstrate that our approach outperforms scalar formulations on the MOT16 benchmark in terms of robustness to noise.Attribution 4.0 International LicenseCCS Concepts: Theory of computation -> Design and analysis of algorithms; Theory and algorithms for application domainsTheory of computationDesign and analysis of algorithmsTheory and algorithms for application domains10.2312/egp.2023102815-162 pages