{"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/adaptive-rotated-convolution-for-rotated","title":"Adaptive Rotated Convolution for Rotated Object Detection","arxiv_id":"2303.07820","date":"2023-03-14","proceeding":"ICCV 2023 1","authors":["Yifan Pu","Yiru Wang","Zhuofan Xia","Yizeng Han","Yulin Wang","Weihao Gan","Zidong Wang","Shiji Song","Gao Huang"],"abstract":"Rotated object detection aims to identify and locate objects in images with arbitrary orientation. In this scenario, the oriented directions of objects vary considerably across different images, while multiple orientations of objects exist within an image. This intrinsic characteristic makes it challenging for standard backbone networks to extract high-quality features of these arbitrarily orientated objects. In this paper, we present Adaptive Rotated Convolution (ARC) module to handle the aforementioned challenges. In our ARC module, the convolution kernels rotate adaptively to extract object features with varying orientations in different images, and an efficient conditional computation mechanism is introduced to accommodate the large orientation variations of objects within an image. The two designs work seamlessly in rotated object detection problem. Moreover, ARC can conveniently serve as a plug-and-play module in various vision backbones to boost their representation ability to detect oriented objects accurately. Experiments on commonly used benchmarks (DOTA and HRSC2016) demonstrate that equipped with our proposed ARC module in the backbone network, the performance of multiple popular oriented object detectors is significantly improved (\\eg +3.03\\% mAP on Rotated RetinaNet and +4.16\\% on CFA). Combined with the highly competitive method Oriented R-CNN, the proposed approach achieves state-of-the-art performance on the DOTA dataset with 81.77\\% mAP. Code is available at \\url{https://github.com/LeapLabTHU/ARC}.","url_abs":"https://arxiv.org/abs/2303.07820v2","url_pdf":"https://arxiv.org/pdf/2303.07820v2.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":"adaptive-rotated-convolution-for-rotated","repo_url":"https://github.com/LeapLabTHU/ARC","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"arc","task_name":"ARC"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-in-aerial-images","task_name":"Object Detection In Aerial Images"},{"task_slug":"oriented-object-detection","task_name":"Oriented Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"fpn","method_name":"FPN"},{"method_slug":"focal-loss","method_name":"Focal Loss"},{"method_slug":"retinanet","method_name":"RetinaNet"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/object-detection-in-aerial-images-on-dota-1","task":"Object Detection In Aerial Images","dataset":"DOTA","model":"ARC","rank_in_archive_order":8,"of":58,"metrics":{"mAP":"81.77%"},"uses_additional_data":true}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2303.07820","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.07820"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"deterministic:regex_extraction","url":"https://github.com/LeapLabTHU/ARC","reach":null}],"summary":{"ran":1,"ran_draft_wrong":1,"ran_fixture":1},"by_repo_kind":{"official":{"samples":3,"ran":3,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"23c7865572f12a2a","entry":"AdaptiveRotatedConv2d","repo":"LeapLabTHU/ARC","repo_kind":"official","path":"mmdet/models/backbones/modules/adaptive_rotated_conv.py","file_url":"https://github.com/LeapLabTHU/ARC/blob/HEAD/mmdet/models/backbones/modules/adaptive_rotated_conv.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"23c7865572f12a2a"}},{"code_sha256_prefix":"986dc40e23e22ad0","entry":"_get_rotation_matrix","repo":"LeapLabTHU/ARC","repo_kind":"official","path":"mmdet/models/backbones/modules/adaptive_rotated_conv.py","file_url":"https://github.com/LeapLabTHU/ARC/blob/HEAD/mmdet/models/backbones/modules/adaptive_rotated_conv.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"986dc40e23e22ad0"}},{"code_sha256_prefix":"f8778db29554c9d8","entry":"batch_rotate_multiweight","repo":"LeapLabTHU/ARC","repo_kind":"official","path":"mmdet/models/backbones/modules/adaptive_rotated_conv.py","file_url":"https://github.com/LeapLabTHU/ARC/blob/HEAD/mmdet/models/backbones/modules/adaptive_rotated_conv.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"f8778db29554c9d8"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}