{"id":"https://openalex.org/W3139036545","doi":"https://doi.org/10.1109/bmsb49480.2020.9379858","title":"Improving Real-world Object Detection Using Balanced Loss","display_name":"Improving Real-world Object Detection Using Balanced Loss","publication_year":2020,"publication_date":"2020-10-27","ids":{"openalex":"https://openalex.org/W3139036545","doi":"https://doi.org/10.1109/bmsb49480.2020.9379858","mag":"3139036545"},"language":"en","primary_location":{"id":"doi:10.1109/bmsb49480.2020.9379858","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bmsb49480.2020.9379858","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)","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/A5003864108","display_name":"Shengyang Shen","orcid":null},"institutions":[{"id":"https://openalex.org/I190752583","display_name":"ParisTech","ror":"https://ror.org/05c2qg481","country_code":"FR","type":"education","lineage":["https://openalex.org/I190752583"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Shengyang Shen","raw_affiliation_strings":["SJTU-ParisTech Elite Institute of Technology"],"affiliations":[{"raw_affiliation_string":"SJTU-ParisTech Elite Institute of Technology","institution_ids":["https://openalex.org/I190752583"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043546100","display_name":"Zexiang Liu","orcid":"https://orcid.org/0000-0001-8020-1619"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zexiang Liu","raw_affiliation_strings":["School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081589900","display_name":"Bingkun Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bingkun Zhao","raw_affiliation_strings":["School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100379221","display_name":"Li Chen","orcid":"https://orcid.org/0000-0002-2300-6996"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Chen","raw_affiliation_strings":["School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023787090","display_name":"Chongyang Zhang","orcid":"https://orcid.org/0000-0001-7292-0445"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chongyang Zhang","raw_affiliation_strings":["School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5003864108"],"corresponding_institution_ids":["https://openalex.org/I190752583"],"apc_list":null,"apc_paid":null,"fwci":0.1954,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.52841635,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"5","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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":0.9998999834060669,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"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":0.9943000078201294,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.7566877603530884},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6799352169036865},{"id":"https://openalex.org/keywords/cross-entropy","display_name":"Cross entropy","score":0.6721444725990295},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.6161254644393921},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.5527721643447876},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5312150716781616},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5096635818481445},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.47733432054519653},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4718955457210541},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.4689806401729584},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44529208540916443},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3311799466609955},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.2410801649093628},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12113136053085327}],"concepts":[{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.7566877603530884},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6799352169036865},{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.6721444725990295},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.6161254644393921},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.5527721643447876},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5312150716781616},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5096635818481445},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.47733432054519653},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4718955457210541},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.4689806401729584},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44529208540916443},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3311799466609955},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.2410801649093628},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12113136053085327},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bmsb49480.2020.9379858","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bmsb49480.2020.9379858","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)","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":26,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W2031454541","https://openalex.org/W2031489346","https://openalex.org/W2108598243","https://openalex.org/W2194775991","https://openalex.org/W2338318698","https://openalex.org/W2490270993","https://openalex.org/W2504335775","https://openalex.org/W2565639579","https://openalex.org/W2613718673","https://openalex.org/W2809784273","https://openalex.org/W2936503027","https://openalex.org/W2938271999","https://openalex.org/W2962721361","https://openalex.org/W2963113370","https://openalex.org/W2963315052","https://openalex.org/W2963351448","https://openalex.org/W2963998989","https://openalex.org/W2990739996","https://openalex.org/W2997747012","https://openalex.org/W3098090606","https://openalex.org/W6620707391","https://openalex.org/W6761662064","https://openalex.org/W6761855697","https://openalex.org/W6770274938"],"related_works":["https://openalex.org/W2802018156","https://openalex.org/W2101531944","https://openalex.org/W4313315626","https://openalex.org/W2922437833","https://openalex.org/W4312696271","https://openalex.org/W4223892596","https://openalex.org/W2933098581","https://openalex.org/W2556125083","https://openalex.org/W3139036545","https://openalex.org/W2994927414"],"abstract_inverted_index":{"Training":[0],"process":[1],"is":[2],"crucial":[3],"to":[4,56],"the":[5,22,28,58,74,97,101],"success":[6],"of":[7,30],"object":[8,46],"detectors.":[9],"Real-world":[10],"datasets":[11],"often":[12],"have":[13],"skewed":[14],"distributions,":[15],"which":[16,49],"results":[17],"in":[18,48],"imbalance":[19,59,75],"issues":[20],"during":[21,60],"training":[23],"process,":[24],"and":[25,66,79,87],"thus":[26],"affects":[27],"performance":[29,103],"detector.":[31],"In":[32],"this":[33],"work,":[34],"we":[35],"propose":[36],"a":[37],"simple":[38],"but":[39],"effective":[40],"framework":[41],"towards":[42],"balanced":[43,51,62,67,98],"learning":[44],"for":[45,72],"detection,":[47],"two":[50],"loss":[52,65,99],"functions":[53],"are":[54],"developed":[55],"alleviate":[57],"training:":[61],"cross":[63],"entropy":[64],"classification":[68],"regression":[69],"loss,":[70],"respectively":[71],"reducing":[73],"at":[76],"positive-negative":[77],"samples":[78],"objective":[80],"level.":[81],"Experiments":[82],"on":[83],"one":[84,88],"pedestrian":[85],"detection":[86,102],"foreign":[89],"particle":[90],"inspection":[91],"task":[92],"show":[93],"that,":[94],"benefitted":[95],"from":[96],"design,":[100],"can":[104],"be":[105],"improved":[106],"significantly.":[107]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
