Papers › Hybrid Proposal Refiner: Revisiting DETR Series from the Faster R-CNN Perspective

Hybrid Proposal Refiner: Revisiting DETR Series from the Faster R-CNN Perspective

1 Jan 2024CVPR 2024 1archive 2025-07-28

Jinjing Zhao, Fangyun Wei, Chang Xu

With the transformative impact of the Transformer DETR pioneered the application of the encoder-decoder architecture to object detection. A collection of follow-up research e.g. Deformable DETR aims to enhance DETR while adhering to the encoder-decoder design. In this work we revisit the DETR series through the lens of Faster R-CNN. We find that the DETR resonates with the underlying principles of Faster R-CNN's RPN-refiner design but benefits from end-to-end detection owing to the incorporation of Hungarian matching. We systematically adapt the Faster R-CNN towards the Deformable DETR by integrating or repurposing each component of Deformable DETR and note that Deformable DETR's improved performance over Faster R-CNN is attributed to the adoption of advanced modules such as a superior proposal refiner (e.g. deformable attention rather than RoI Align). When viewing the DETR through the RPN-refiner paradigm we delve into various proposal refinement techniques such as deformable attention cross attention and dynamic convolution. These proposal refiners cooperate well with each other; thus we synergistically combine them to establish a Hybrid Proposal Refiner (HPR). Our HPR is versatile and can be incorporated into various DETR detectors. For instance by integrating HPR to a strong DETR detector we achieve an AP of 54.9 on the COCO benchmark utilizing a ResNet-50 backbone and a 36-epoch training schedule. Code and models are available at https://github.com/ZhaoJingjing713/HPR.

PaperPDFCode

Code

zhaojingjing713/hpr officialmentioned in paperpytorchMIT report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

Tasks

DecoderObject Detectionobject-detection

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Methods

Absolute Position EncodingsAdamAttentionBPEConvolutionDeformable Attention ModuleDeformable DETRDense ConnectionsDetrDropoutFaster R-CNNFeedforward NetworkLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerRPNResidual ConnectionRoIPoolSoftmaxTransformer

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections