Non-Separable Multi-Dimensional Network Flows for Visual Computing

• URL: http://arxiv.org/abs/2305.08628v1
• Date: Mon, 15 May 2023 13:21:44 GMT
• Title: Non-Separable Multi-Dimensional Network Flows for Visual Computing
• Authors: Viktoria Ehm, Daniel Cremers, Florian Bernard
• Abstract summary: We propose a novel formalism for non-separable multi-dimensional network flows. 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.
• Score: 62.50191141358778
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 high-dimensional 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.

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