We propose an extremely simple and highly effective approach to faithfully combine different object detectors to obtain a Mixture of Experts (MoE) that has a superior accuracy to the individual experts in the mixture. We find that naively combining these experts in a similar way to the well-known Deep Ensembles (DEs), does not result in an effective MoE. We identify the incompatibility between the confidence score distribution of different detectors to be the primary reason for such failure cases. Therefore, to construct the MoE, our proposal is to first calibrate each individual detector against a target calibration function. Then, filter and refine all the predictions from different detectors in the mixture. We term this approach as MoCaE and demonstrate its effectiveness through extensive experiments on object detection, instance segmentation and rotated object detection tasks. Specifically, MoCaE improves (i) three strong object detectors on COCO test-dev by $2.4$ $\mathrm{AP}$ by reaching $59.0$ $\mathrm{AP}$; (ii) instance segmentation methods on the challenging long-tailed LVIS dataset by $2.3$ $\mathrm{AP}$; and (iii) all existing rotated object detectors by reaching $82.62$ $\mathrm{AP_{50}}$ on DOTA dataset, establishing a new state-of-the-art (SOTA). Code will be made public.

翻译：我们提出一种极其简单且高效的方法，用于可靠地组合不同目标检测器以构建混合专家（MoE），其精度显著优于混合中的单个专家。研究发现，若采用与经典深度集成（DEs）类似的方式直接组合这些专家，无法获得有效的MoE。我们指出不同检测器置信度分数分布的不兼容性是导致此类失败的主要原因。因此，为构建MoE，我们建议首先针对目标校准函数对每个检测器进行独立校准，随后滤除并精炼混合中不同检测器的所有预测结果。我们将该方法命名为MoCaE，并通过在目标检测、实例分割及旋转目标检测任务上的大量实验验证其有效性。具体而言，MoCaE实现了以下改进：（i）在COCO test-dev数据集上，将三个强检测器提升2.4个AP，达到59.0 AP；（ii）在具有挑战性的长尾LVIS数据集上，实例分割方法提升2.3 AP；（iii）在DOTA数据集上，所有现有旋转目标检测器达到82.62 AP₅₀，创下新的最先进（SOTA）水平。代码将公开发布。