{"id":"https://openalex.org/W2962677013","doi":"https://doi.org/10.1109/cvpr.2019.00300","title":"Bounding Box Regression With Uncertainty for Accurate Object Detection","display_name":"Bounding Box Regression With Uncertainty for Accurate Object Detection","publication_year":2019,"publication_date":"2019-06-01","ids":{"openalex":"https://openalex.org/W2962677013","doi":"https://doi.org/10.1109/cvpr.2019.00300","mag":"2962677013"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2019.00300","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2019.00300","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5076586872","display_name":"Yihui He","orcid":"https://orcid.org/0000-0002-1057-6826"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yihui He","raw_affiliation_strings":["Carnegie Mellon University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101087504","display_name":"Chenchen Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chenchen Zhu","raw_affiliation_strings":["Carnegie Mellon University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102939225","display_name":"Jianren Wang","orcid":"https://orcid.org/0000-0001-9350-1813"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jianren Wang","raw_affiliation_strings":["Carnegie Mellon University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057959136","display_name":"Marios Savvides","orcid":null},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Marios Savvides","raw_affiliation_strings":["Carnegie Mellon University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100362465","display_name":"Xiangyu Zhang","orcid":"https://orcid.org/0000-0003-2138-4608"},"institutions":[{"id":"https://openalex.org/I4401726805","display_name":"Megvii (China)","ror":"https://ror.org/040b32p69","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726805"]}],"countries":[],"is_corresponding":false,"raw_author_name":"Xiangyu Zhang","raw_affiliation_strings":["Megvii Inc. (Face++)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Megvii Inc. (Face++)","institution_ids":["https://openalex.org/I4401726805"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5076586872"],"corresponding_institution_ids":["https://openalex.org/I74973139"],"apc_list":null,"apc_paid":null,"fwci":35.0054,"has_fulltext":false,"cited_by_count":588,"citation_normalized_percentile":{"value":0.99801465,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"2883","last_page":"2892"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/minimum-bounding-box","display_name":"Minimum bounding box","score":0.9344128966331482},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.8506093621253967},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6845787763595581},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.6109322905540466},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.6034618020057678},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5405489802360535},{"id":"https://openalex.org/keywords/merge","display_name":"Merge (version control)","score":0.5370411276817322},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.4962068200111389},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4711174964904785},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.47015950083732605},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.46999797224998474},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45527830719947815},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.254042387008667},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.187098890542984},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.15951421856880188},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.10907813906669617}],"concepts":[{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.9344128966331482},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.8506093621253967},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6845787763595581},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.6109322905540466},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.6034618020057678},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5405489802360535},{"id":"https://openalex.org/C197129107","wikidata":"https://www.wikidata.org/wiki/Q1921621","display_name":"Merge (version control)","level":2,"score":0.5370411276817322},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.4962068200111389},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4711174964904785},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.47015950083732605},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.46999797224998474},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45527830719947815},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.254042387008667},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.187098890542984},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.15951421856880188},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.10907813906669617},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cvpr.2019.00300","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2019.00300","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":81,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1667652561","https://openalex.org/W1686810756","https://openalex.org/W1861492603","https://openalex.org/W1887369574","https://openalex.org/W1932624639","https://openalex.org/W1968245656","https://openalex.org/W2028499920","https://openalex.org/W2046871815","https://openalex.org/W2102605133","https://openalex.org/W2108598243","https://openalex.org/W2126096326","https://openalex.org/W2151103935","https://openalex.org/W2155893237","https://openalex.org/W2186094539","https://openalex.org/W2194775991","https://openalex.org/W2407521645","https://openalex.org/W2504335775","https://openalex.org/W2565639579","https://openalex.org/W2592463526","https://openalex.org/W2600383743","https://openalex.org/W2601564443","https://openalex.org/W2612624696","https://openalex.org/W2613718673","https://openalex.org/W2618765862","https://openalex.org/W2622263826","https://openalex.org/W2769291631","https://openalex.org/W2773653677","https://openalex.org/W2798463715","https://openalex.org/W2798542761","https://openalex.org/W2808278571","https://openalex.org/W2884561390","https://openalex.org/W2886617718","https://openalex.org/W2886851211","https://openalex.org/W2886904239","https://openalex.org/W2891752445","https://openalex.org/W2922438466","https://openalex.org/W2949941638","https://openalex.org/W2950800384","https://openalex.org/W2963037989","https://openalex.org/W2963125010","https://openalex.org/W2963150697","https://openalex.org/W2963167203","https://openalex.org/W2963299996","https://openalex.org/W2963363373","https://openalex.org/W2963677766","https://openalex.org/W2964080601","https://openalex.org/W2964121718","https://openalex.org/W2964241181","https://openalex.org/W2970594690","https://openalex.org/W3012573144","https://openalex.org/W3106250896","https://openalex.org/W4295109175","https://openalex.org/W4302296459","https://openalex.org/W6620707391","https://openalex.org/W6631782140","https://openalex.org/W6637151318","https://openalex.org/W6637373629","https://openalex.org/W6639102338","https://openalex.org/W6678588784","https://openalex.org/W6686583229","https://openalex.org/W6714138976","https://openalex.org/W6735443497","https://openalex.org/W6738279954","https://openalex.org/W6739622702","https://openalex.org/W6742348326","https://openalex.org/W6745637532","https://openalex.org/W6746085279","https://openalex.org/W6746314684","https://openalex.org/W6746975906","https://openalex.org/W6750697433","https://openalex.org/W6752007561","https://openalex.org/W6753376238","https://openalex.org/W6753490951","https://openalex.org/W6753494528","https://openalex.org/W6753610190","https://openalex.org/W6754130498","https://openalex.org/W6754372794","https://openalex.org/W6785652829","https://openalex.org/W6891880107"],"related_works":["https://openalex.org/W4237171675","https://openalex.org/W3036286480","https://openalex.org/W4287027631","https://openalex.org/W3192357901","https://openalex.org/W2387360586","https://openalex.org/W2952736415","https://openalex.org/W3209723314","https://openalex.org/W3205398323","https://openalex.org/W2883297582","https://openalex.org/W4390524233"],"abstract_inverted_index":{"Large-scale":[0],"object":[1],"detection":[2],"datasets":[3],"(e.g.,":[4],"MS-COCO)":[5],"try":[6],"to":[7,72,101],"define":[8],"the":[9,28,55,84,91,112],"ground":[10],"truth":[11],"bounding":[12,29,38,44,75,126],"boxes":[13,76],"as":[14,16],"clear":[15],"possible.":[17],"However,":[18],"we":[19,34,89],"observe":[20],"that":[21],"ambiguities":[22],"are":[23,134],"still":[24],"introduced":[25],"when":[26],"labeling":[27],"boxes.":[30],"In":[31],"this":[32],"paper,":[33],"propose":[35],"a":[36],"novel":[37],"box":[39,45,127],"regression":[40],"loss":[41,52],"for":[42,105],"learning":[43],"transformation":[46],"and":[47,114,118,132],"localization":[48,56,68,85],"variance":[49,69],"together.":[50],"Our":[51,130],"greatly":[53],"improves":[54,83,111],"accuracies":[57],"of":[58,95],"various":[59],"architectures":[60],"with":[61],"nearly":[62],"no":[63],"additional":[64],"computation.":[65],"The":[66],"learned":[67],"allows":[70],"us":[71],"merge":[73],"neighboring":[74],"during":[77],"non-maximum":[78],"suppression":[79],"(NMS),":[80],"which":[81,121],"further":[82],"performance.":[86],"On":[87],"MS-COCO,":[88],"boost":[90],"Average":[92],"Precision":[93],"(AP)":[94],"VGG-16":[96],"Faster":[97],"R-CNN":[98],"from":[99],"23.6%":[100],"29.1%.":[102],"More":[103],"importantly,":[104],"ResNet-50-FPN":[106],"Mask":[107],"R-CNN,":[108],"our":[109],"method":[110],"AP":[113],"AP90":[115],"by":[116],"1.8%":[117],"6.2%":[119],"respectively,":[120],"significantly":[122],"outperforms":[123],"previous":[124],"state-of-the-art":[125],"refinement":[128],"methods.":[129],"code":[131],"models":[133],"available":[135],"at":[136],"github.com/yihui-he/KL-Loss.":[137]},"counts_by_year":[{"year":2026,"cited_by_count":13},{"year":2025,"cited_by_count":57},{"year":2024,"cited_by_count":74},{"year":2023,"cited_by_count":99},{"year":2022,"cited_by_count":99},{"year":2021,"cited_by_count":133},{"year":2020,"cited_by_count":95},{"year":2019,"cited_by_count":16},{"year":2018,"cited_by_count":2}],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2025-10-10T00:00:00"}
