  The query mechanism introduced in the DETR method is changing the paradigm of
object detection and recently there are many query-based methods have obtained
strong object detection performance. However, the current query-based detection
pipelines suffer from the following two issues. Firstly, multi-stage decoders
are required to optimize the randomly initialized object queries, incurring a
large computation burden. Secondly, the queries are fixed after training,
leading to unsatisfying generalization capability. To remedy the above issues,
we present featurized object queries predicted by a query generation network in
the well-established Faster R-CNN framework and develop a Featurized Query
R-CNN. Extensive experiments on the COCO dataset show that our Featurized Query
R-CNN obtains the best speed-accuracy trade-off among all R-CNN detectors,
including the recent state-of-the-art Sparse R-CNN detector. The code is
available at {https://github.com/hustvl/Featurized-QueryRCNN.
