mlsanityData to 7 Oct 2026

Featurized Query R-CNN

arXiv 2206.06258 ↗First posted 13 Jun 2022cs.CVcited by 4

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 34 of its 39 references that are in the map. First posted on arXiv 13 Jun 2022.

Builds on

34 of 39 references.

References · 34

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22 more
  1. 2017Focal Loss for Dense Object Detectioncited by 749
  2. 2022DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETRcited by 569
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Built on by

Cited by year

Citing papers per year: 2023: 3; 2024: 0; 2025: 1.

4 papers.

  1. 2023FeatEnHancer: Enhancing Hierarchical Features for Object Detection and Beyond Under Low-Light Visioncited by 22
  2. 2023ASAG: Building Strong One-Decoder-Layer Sparse Detectors via Adaptive Sparse Anchor Generationcited by 6
  3. 2025Dark-ISP: Enhancing RAW Image Processing for Low-Light Object Detectioncited by 5
  4. 2023Can the Query-based Object Detector Be Designed with Fewer Stages?cited by 0