{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/hybrid-proposal-refiner-revisiting-detr","title":"Hybrid Proposal Refiner: Revisiting DETR Series from the Faster R-CNN Perspective","arxiv_id":null,"date":"2024-01-01","proceeding":"CVPR 2024 1","authors":["Jinjing Zhao","Fangyun Wei","Chang Xu"],"abstract":"    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.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2024/html/Zhao_Hybrid_Proposal_Refiner_Revisiting_DETR_Series_from_the_Faster_R-CNN_CVPR_2024_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2024/papers/Zhao_Hybrid_Proposal_Refiner_Revisiting_DETR_Series_from_the_Faster_R-CNN_CVPR_2024_paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"hybrid-proposal-refiner-revisiting-detr","repo_url":"https://github.com/zhaojingjing713/hpr","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"deformable-attention-module","method_name":"Deformable Attention Module"},{"method_slug":"deformable-detr","method_name":"Deformable DETR"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"detr","method_name":"Detr"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"faster-r-cnn","method_name":"Faster R-CNN"},{"method_slug":"feedforward-network","method_name":"Feedforward Network"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"rpn","method_name":"RPN"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"roipool","method_name":"RoIPool"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}