{"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/robust-region-feature-synthesizer-for-zero","title":"Robust Region Feature Synthesizer for Zero-Shot Object Detection","arxiv_id":"2201.00103","date":"2022-01-01","proceeding":"CVPR 2022 1","authors":["Peiliang Huang","Junwei Han","De Cheng","Dingwen Zhang"],"abstract":"Zero-shot object detection aims at incorporating class semantic vectors to realize the detection of (both seen and) unseen classes given an unconstrained test image. In this study, we reveal the core challenges in this research area: how to synthesize robust region features (for unseen objects) that are as intra-class diverse and inter-class separable as the real samples, so that strong unseen object detectors can be trained upon them. To address these challenges, we build a novel zero-shot object detection framework that contains an Intra-class Semantic Diverging component and an Inter-class Structure Preserving component. The former is used to realize the one-to-more mapping to obtain diverse visual features from each class semantic vector, preventing miss-classifying the real unseen objects as image backgrounds. While the latter is used to avoid the synthesized features too scattered to mix up the inter-class and foreground-background relationship. To demonstrate the effectiveness of the proposed approach, comprehensive experiments on PASCAL VOC, COCO, and DIOR datasets are conducted. Notably, our approach achieves the new state-of-the-art performance on PASCAL VOC and COCO and it is the first study to carry out zero-shot object detection in remote sensing imagery.","url_abs":"https://arxiv.org/abs/2201.00103v1","url_pdf":"https://arxiv.org/pdf/2201.00103v1.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":"robust-region-feature-synthesizer-for-zero","repo_url":"https://github.com/HPL123/RRFS","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"generalized-zero-shot-object-detection","task_name":"Generalized Zero-Shot Object Detection"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"zero-shot-object-detection","task_name":"Zero-Shot Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/zero-shot-object-detection-on-ms-coco","task":"Zero-Shot Object Detection","dataset":"MS-COCO","model":"ZSD-RRFS","rank_in_archive_order":4,"of":9,"metrics":{"Recall":"62.3","mAP":"19.8"},"uses_additional_data":false},{"leaderboard":"/sota/zero-shot-object-detection-on-pascal-voc-07","task":"Zero-Shot Object Detection","dataset":"PASCAL VOC'07","model":"RRFS-ZSD","rank_in_archive_order":2,"of":7,"metrics":{"mAP":"65.50"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2201.00103","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.00103"}},"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/HPL123/RRFS","reach":null}],"summary":{"ran":1,"unverified":1},"by_repo_kind":{"official":{"samples":2,"ran":1,"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":2,"samples":[{"code_sha256_prefix":"9f2ec3a342b4f5ed","entry":"MLP_SKIP_G","repo":"HPL123/RRFS","repo_kind":"official","path":"model.py","file_url":"https://github.com/HPL123/RRFS/blob/HEAD/model.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"9f2ec3a342b4f5ed"}},{"code_sha256_prefix":"b661b76a7321fedd","entry":"weights_init","repo":"HPL123/RRFS","repo_kind":"official","path":"model.py","file_url":"https://github.com/HPL123/RRFS/blob/HEAD/model.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"b661b76a7321fedd"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}