We present a determinantal point process (DPP) inspired alternative to non-maximumsuppression (NMS) which has become an integral step in all state-of-the-art object de-tection frameworks. DPPs have been shown to encourage diversity in subset selectionproblems. We pose NMS as a subset selection problem and posit that directly incor-porating DPP like framework can improve the overall performance of the object detectionsystem. We propose an optimization problem which takes the same inputs as NMS, butintroduces a novel sub-modularity based diverse subset selection functional. Our resultsstrongly indicate that the modifications proposed in this paper can provide consistentimprovements to state-of-the-art object detection pipelines.
Recommended citation: Some, Samik, Mithun Das Gupta, and Vinay P. Namboodiri. “Determinantal Point Process as an alternative to NMS.” Proceedings of British Machine Vision Conference (2020), arXiv preprint arXiv:2008.11451 (2020).