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.

翻译：网络（或图）中的流在许多计算机视觉任务中扮演着重要角色。这些图中的标量边常导致信息损失，从而限制了表达力。例如，高维数据（如特征描述子）常被映射为单一标量值（如两个特征描述子之间的相似度）。为克服这一局限，我们提出了一种用于不可分离多维网络流的全新形式化框架。通过该框架，我们实现了一种自动且自适应的特征选择策略——由于流是基于每个维度定义的，最大化流会自动选择最佳匹配的特征维度。作为概念验证，我们将该形式化方法应用于多目标跟踪问题，并证明在MOT16基准上，我们的方法在噪声鲁棒性方面优于标量形式化方法。