What Are Expected Queries in End-to-End Object Detection?

• URL: http://arxiv.org/abs/2206.01232v1
• Date: Thu, 2 Jun 2022 18:15:44 GMT
• Title: What Are Expected Queries in End-to-End Object Detection?
• Authors: Shilong Zhang, Xinjiang Wang, Jiaqi Wang, Jiangmiao Pang and Kai Chen
• Abstract summary: This paper shows that the expected queries should be COCO Distinct Queries (DDQ) DDQ is stronger, more robust, and converges faster than previous methods. It obtains 44.5 AP on the MSarity detection dataset with only 12 epochs.
• Score: 28.393693394478724
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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}.

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