  Recent end-to-end multi-object detectors simplify the inference pipeline by
removing hand-crafted processes such as non-maximum suppression (NMS). However,
during training, they still heavily rely on heuristics and hand-crafted
processes which deteriorate the reliability of the predicted confidence score.
In this paper, we propose a novel framework to train an end-to-end multi-object
detector consisting of only two terms: negative log-likelihood (NLL) and a
regularization term. In doing so, the multi-object detection problem is treated
as density estimation of the ground truth bounding boxes utilizing a
regularized mixture density model. The proposed \textit{end-to-end multi-object
Detection with a Regularized Mixture Model} (D-RMM) is trained by minimizing
the NLL with the proposed regularization term, maximum component maximization
(MCM) loss, preventing duplicate predictions. Our method reduces the heuristics
of the training process and improves the reliability of the predicted
confidence score. Moreover, our D-RMM outperforms the previous end-to-end
detectors on MS COCO dataset.
