Non-Separable Multi-Dimensional Network Flows for Visual Computing
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Date
2023
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Flows 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.
Description
CCS Concepts: Theory of computation -> Design and analysis of algorithms; Theory and algorithms for application domains
@inproceedings{10.2312:egp.20231028,
booktitle = {Eurographics 2023 - Posters},
editor = {Singh, Gurprit and Chu, Mengyu (Rachel)},
title = {{Non-Separable Multi-Dimensional Network Flows for Visual Computing}},
author = {Ehm, Viktoria and Cremers, Daniel and Bernard, Florian},
year = {2023},
publisher = {The Eurographics Association},
ISSN = {1017-4656},
ISBN = {978-3-03868-211-0},
DOI = {10.2312/egp.20231028}
}