We present a determinantal point process (DPP) inspired alternative to non-maximum suppression (NMS) which has become an integral step in all state-of-the-art object detection frameworks. DPPs have been shown to encourage diversity in subset selection problems. We pose NMS as a subset selection problem and posit that directly incorporating DPP like framework can improve the overall performance of the object detection system. We propose an optimization problem which takes the same inputs as NMS, but introduces a novel sub-modularity based diverse subset selection functional. Our results strongly indicate that the modifications proposed in this paper can provide consistent improvements to state-of-the-art object detection pipelines.

翻译：本文提出一种受行列式点过程启发的非极大值抑制替代方法，后者已成为所有先进目标检测框架中不可或缺的步骤。行列式点过程已被证明能够促进子集选择问题中的多样性。我们将非极大值抑制构建为子集选择问题，并提出直接引入类行列式点过程框架能够提升目标检测系统的整体性能。我们设计了一个优化问题，该问题采用与非极大值抑制相同的输入，但引入了基于子模函数的多样性子集选择泛函。实验结果充分表明，本文提出的改进方案能够为先进目标检测流程带来持续的性能提升。