  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}.
