在端到端目标检测中，期望的性能是什么？
What Are Expected Queries in End-to-End Object Detection?
摘要：
端到端的目标检测在DETR出现后得到了迅速的发展。DETR使用一组稀疏查询来取代大多数传统检测器中的密集候选框。相比之下，稀疏查询不能保证高召回密集先验。然而，在当前的框架中，使查询密集并不是微不足道的。它不仅计算量大，而且优化困难。由于稀疏查询和密集查询都是不完美的，那么\emph{what are expected queries in end-to-end object detection}？本文表明，预期的查询应该是密集的独特的查询（DDQ）。具体地说，我们引入密集先验的框架生成密集查询。重复查询移除预处理应用于这些查询，使得它们彼此可区分。然后迭代地处理密集的不同查询以获得最终的稀疏输出。我们表明，DDQ是更强大，更强大，收敛速度更快。它在MS COCO检测数据集上仅用12个时期获得44.5 AP。DDQ也是鲁棒的，因为它在各种数据集上的目标检测和实例分割任务上都优于以前的方法。DDQ融合了传统的密集先验和最近的端到端检测器的优点。我们希望它可以作为一个新的基线，并激励研究人员重新审视传统方法和端到端检测器之间的互补性。源代码可在\url{https://github.com/jshilong/DDQ}上公开获得。
英文摘要：
End-to-end object detection is rapidly progressed after the emergence of DETR. DETRs use a set of sparse queries that replace the dense candidate boxes in most traditional detectors. In comparison, the sparse queries cannot guarantee a high recall as dense priors. However, making queries dense is not trivial in current frameworks. It not only suffers from heavy computational cost but also difficult optimization. As both sparse and dense queries are imperfect, then \emph{what are expected queries in end-to-end object detection}? This paper shows that the expected queries should be Dense Distinct Queries (DDQ). Concretely, we introduce dense priors back to the framework to generate dense queries. A duplicate query removal pre-process is applied to these queries so that they are distinguishable from each other. The dense distinct queries are then iteratively processed to obtain final sparse outputs. We show that DDQ is stronger, more robust, and converges faster. It obtains 44.5 AP on the MS COCO detection dataset with only 12 epochs. DDQ is also robust as it outperforms previous methods on both object detection and instance segmentation tasks on various datasets. DDQ blends advantages from traditional dense priors and recent end-to-end detectors. We hope it can serve as a new baseline and inspires researchers to revisit the complementarity between traditional methods and end-to-end detectors. The source code is publicly available at \url{https://github.com/jshilong/DDQ}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
