{"id":"https://openalex.org/W3161083802","doi":"https://doi.org/10.1109/icassp39728.2021.9413641","title":"A Plug and Play Fast Intersection Over Union Loss for Boundary Box Regression","display_name":"A Plug and Play Fast Intersection Over Union Loss for Boundary Box Regression","publication_year":2021,"publication_date":"2021-05-13","ids":{"openalex":"https://openalex.org/W3161083802","doi":"https://doi.org/10.1109/icassp39728.2021.9413641","mag":"3161083802"},"language":"en","primary_location":{"id":"doi:10.1109/icassp39728.2021.9413641","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp39728.2021.9413641","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5028783187","display_name":"Zengsheng Kuang","orcid":null},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zengsheng Kuang","raw_affiliation_strings":["College of Computer Science, Nankai University, Tianjin, China","TJ Key Lab of NDST, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science, Nankai University, Tianjin, China","institution_ids":["https://openalex.org/I205237279"]},{"raw_affiliation_string":"TJ Key Lab of NDST, Tianjin, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060658571","display_name":"Xian Fang","orcid":"https://orcid.org/0000-0001-5161-2574"},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xian Fang","raw_affiliation_strings":["College of Computer Science, Nankai University, Tianjin, China","TJ Key Lab of NDST, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science, Nankai University, Tianjin, China","institution_ids":["https://openalex.org/I205237279"]},{"raw_affiliation_string":"TJ Key Lab of NDST, Tianjin, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045614813","display_name":"Ruixun Zhang","orcid":"https://orcid.org/0000-0002-7670-8393"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ruixun Zhang","raw_affiliation_strings":["MIT Laboratory for Financial Engineering, Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"MIT Laboratory for Financial Engineering, Cambridge, MA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101125772","display_name":"Xiuli Shao","orcid":"https://orcid.org/0000-0001-5059-3172"},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiuli Shao","raw_affiliation_strings":["College of Computer Science, Nankai University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science, Nankai University, Tianjin, China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100608725","display_name":"Hongpeng Wang","orcid":"https://orcid.org/0000-0003-4488-1375"},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongpeng Wang","raw_affiliation_strings":["College of Artificial Intelligence, Nankai University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"College of Artificial Intelligence, Nankai University, Tianjin, China","institution_ids":["https://openalex.org/I205237279"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5028783187"],"corresponding_institution_ids":["https://openalex.org/I205237279"],"apc_list":null,"apc_paid":null,"fwci":0.4803,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.63797386,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1705","last_page":"1709"},"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":0.9994999766349792,"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":0.9987999796867371,"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/benchmark","display_name":"Benchmark (surveying)","score":0.7482233047485352},{"id":"https://openalex.org/keywords/minimum-bounding-box","display_name":"Minimum bounding box","score":0.7140506505966187},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.6773377656936646},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6627902984619141},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.6570354700088501},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6379603743553162},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.6235271096229553},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.5791728496551514},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.5350421667098999},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5114818811416626},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4433450400829315},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39376652240753174},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35478752851486206},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.20518264174461365},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20019322633743286},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.14447587728500366}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7482233047485352},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.7140506505966187},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.6773377656936646},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6627902984619141},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.6570354700088501},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6379603743553162},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.6235271096229553},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.5791728496551514},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.5350421667098999},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5114818811416626},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4433450400829315},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39376652240753174},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35478752851486206},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.20518264174461365},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20019322633743286},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14447587728500366},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp39728.2021.9413641","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp39728.2021.9413641","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320337504","display_name":"Research and Development","ror":"https://ror.org/027s68j25"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1686810756","https://openalex.org/W1861492603","https://openalex.org/W2031489346","https://openalex.org/W2102605133","https://openalex.org/W2194775991","https://openalex.org/W2504335775","https://openalex.org/W2565639579","https://openalex.org/W2570343428","https://openalex.org/W2579985080","https://openalex.org/W2612624696","https://openalex.org/W2613718673","https://openalex.org/W2796347433","https://openalex.org/W2962766617","https://openalex.org/W2963037989","https://openalex.org/W2963769056","https://openalex.org/W2963857746","https://openalex.org/W2964121718","https://openalex.org/W2964241181","https://openalex.org/W2964444661","https://openalex.org/W2982770724","https://openalex.org/W2997747012","https://openalex.org/W3012573144","https://openalex.org/W3034427487","https://openalex.org/W3098090606","https://openalex.org/W3106250896","https://openalex.org/W4293584584","https://openalex.org/W6620707391","https://openalex.org/W6637373629","https://openalex.org/W6639102338","https://openalex.org/W6732243160","https://openalex.org/W6750227808","https://openalex.org/W6785652829","https://openalex.org/W6948074861"],"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":{"Bounding":[0],"box":[1],"regression":[2,19],"is":[3,104],"a":[4,53,86],"very":[5],"effective":[6],"method":[7,103],"to":[8,42,81,88,106],"improve":[9,89],"the":[10,17,37,65,70,90],"localization":[11,91],"accuracy":[12],"of":[13,72],"object":[14,26],"detection.":[15],"Recently,":[16],"IoU-based":[18,73],"losses":[20],"have":[21],"been":[22],"widely":[23],"used":[24],"in":[25,36],"detection":[27],"algorithms.":[28],"However,":[29],"we":[30,51],"observe":[31],"that":[32,101],"they":[33],"degenerate":[34],"seriously":[35],"late":[38],"training":[39],"period,":[40],"leading":[41],"slow":[43],"convergence":[44],"and":[45],"inaccurate":[46],"localization.":[47],"In":[48],"this":[49],"paper,":[50],"design":[52],"Fast":[54],"Intersection":[55],"over":[56],"Union":[57],"(FIoU)":[58],"loss,":[59],"which":[60],"can":[61,77],"not":[62],"only":[63],"keep":[64],"advantages":[66],"but":[67],"also":[68],"solve":[69],"weakness":[71],"losses.":[74],"Furthermore,":[75],"FIoU":[76],"be":[78],"directly":[79],"applied":[80],"Non-Maximum":[82],"Suppression":[83],"(NMS)":[84],"as":[85],"criterion":[87],"performance.":[92],"Numerous":[93],"experiments":[94],"on":[95],"two":[96],"popular":[97],"benchmark":[98],"datasets":[99],"show":[100],"our":[102],"superior":[105],"other":[107],"the-state-of-art":[108],"methods.":[109]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}