{"id":"https://openalex.org/W4312584711","doi":"https://doi.org/10.1109/access.2022.3215534","title":"Collection-CAM: A Faster Region-Based Saliency Method Using Collection-Wise Mask Over Pyramidal Features","display_name":"Collection-CAM: A Faster Region-Based Saliency Method Using Collection-Wise Mask Over Pyramidal Features","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4312584711","doi":"https://doi.org/10.1109/access.2022.3215534"},"language":"en","primary_location":{"id":"doi:10.1109/access.2022.3215534","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3215534","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09924199.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09924199.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5054923848","display_name":"Yun-Gi Ha","orcid":"https://orcid.org/0000-0001-6437-6988"},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yungi Ha","raw_affiliation_strings":["School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea"],"raw_orcid":"https://orcid.org/0000-0001-6437-6988","affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea","institution_ids":["https://openalex.org/I157485424"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045403869","display_name":"Chan\u2010Hyun Youn","orcid":"https://orcid.org/0000-0002-3970-7308"},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Chan-Hyun Youn","raw_affiliation_strings":["School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea"],"raw_orcid":"https://orcid.org/0000-0002-3970-7308","affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea","institution_ids":["https://openalex.org/I157485424"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I157485424"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0979,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.39064252,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"10","issue":null,"first_page":"112776","last_page":"112788"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9994999766349792,"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":0.9994999766349792,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9983999729156494,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9962999820709229,"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/computer-science","display_name":"Computer science","score":0.8157026171684265},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6582542657852173},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6145873665809631},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.5333813428878784},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5036997199058533},{"id":"https://openalex.org/keywords/data-collection","display_name":"Data collection","score":0.4980628490447998},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4835755228996277},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4752247631549835},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4165400564670563},{"id":"https://openalex.org/keywords/partition","display_name":"Partition (number theory)","score":0.4151352643966675},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.0776033103466034},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.0700870156288147}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8157026171684265},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6582542657852173},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6145873665809631},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.5333813428878784},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5036997199058533},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.4980628490447998},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4835755228996277},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4752247631549835},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4165400564670563},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.4151352643966675},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0776033103466034},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.0700870156288147},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2022.3215534","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3215534","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09924199.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:0d33f9e43ada4dfb93df756afc335e48","is_oa":true,"landing_page_url":"https://doaj.org/article/0d33f9e43ada4dfb93df756afc335e48","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 10, Pp 112776-112788 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2022.3215534","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3215534","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09924199.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.8199999928474426,"display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G1211546875","display_name":null,"funder_award_id":"915027201","funder_id":"https://openalex.org/F4320323103","funder_display_name":"Agency for Defense Development"},{"id":"https://openalex.org/G1701955658","display_name":null,"funder_award_id":"22ZS1100","funder_id":"https://openalex.org/F4320322093","funder_display_name":"Electronics and Telecommunications Research Institute"}],"funders":[{"id":"https://openalex.org/F4320322093","display_name":"Electronics and Telecommunications Research Institute","ror":"https://ror.org/03ysstz10"},{"id":"https://openalex.org/F4320323103","display_name":"Agency for Defense Development","ror":"https://ror.org/05fhe0r85"},{"id":"https://openalex.org/F4320332195","display_name":"Samsung","ror":"https://ror.org/04w3jy968"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4312584711.pdf","grobid_xml":"https://content.openalex.org/works/W4312584711.grobid-xml"},"referenced_works_count":40,"referenced_works":["https://openalex.org/W1797268635","https://openalex.org/W1980038761","https://openalex.org/W1999478155","https://openalex.org/W2016381774","https://openalex.org/W2088049833","https://openalex.org/W2117539524","https://openalex.org/W2123045220","https://openalex.org/W2157331557","https://openalex.org/W2194775991","https://openalex.org/W2257979135","https://openalex.org/W2295107390","https://openalex.org/W2591954064","https://openalex.org/W2594633041","https://openalex.org/W2765793020","https://openalex.org/W2776207810","https://openalex.org/W2809136100","https://openalex.org/W2939181282","https://openalex.org/W2952626150","https://openalex.org/W2953073956","https://openalex.org/W2962858109","https://openalex.org/W2964231383","https://openalex.org/W2969880113","https://openalex.org/W3015214599","https://openalex.org/W3020975691","https://openalex.org/W3035253074","https://openalex.org/W3083948783","https://openalex.org/W3102564565","https://openalex.org/W3176482836","https://openalex.org/W4245436919","https://openalex.org/W4293861706","https://openalex.org/W4300235091","https://openalex.org/W6638319203","https://openalex.org/W6672596467","https://openalex.org/W6687483927","https://openalex.org/W6724569667","https://openalex.org/W6734194636","https://openalex.org/W6739575509","https://openalex.org/W6754669440","https://openalex.org/W6768236498","https://openalex.org/W6776204712"],"related_works":["https://openalex.org/W4298130764","https://openalex.org/W2804364458","https://openalex.org/W2132641928","https://openalex.org/W4310225030","https://openalex.org/W2090259340","https://openalex.org/W1926736923","https://openalex.org/W2186048469","https://openalex.org/W2158836806","https://openalex.org/W2393816671","https://openalex.org/W2083665254"],"abstract_inverted_index":{"Due":[0],"to":[1,27,122,136,139,189],"the":[2,24,29,56,71,88,92,97,107,114,124,140,143,146,167],"black-box":[3],"nature":[4],"of":[5,10,96,191],"deep":[6,25,162],"networks,":[7],"making":[8],"explanations":[9],"their":[11,137],"decision-making":[12],"is":[13,18],"extremely":[14],"challenging.":[15],"A":[16],"solution":[17],"using":[19,54],"post-hoc":[20],"attention":[21,76,147],"mechanisms":[22],"with":[23,78,176],"network":[26,163],"verify":[28],"decision":[30],"basis.":[31],"However,":[32],"those":[33,190],"methods":[34,48],"have":[35,50],"problems":[36],"such":[37],"as":[38],"gradient":[39],"noise":[40],"and":[41,102,118,157,160],"false":[42,125],"confidence.":[43],"In":[44,66],"addition,":[45],"existing":[46],"saliency":[47,174,193],"either":[49],"limited":[51],"performance":[52],"by":[53],"only":[55,170],"last":[57],"convolution":[58],"layer":[59],"or":[60],"suffer":[61],"from":[62],"large":[63],"computational":[64,80,186],"overhead.":[65],"this":[67],"work,":[68],"we":[69],"propose":[70],"Collection-CAM,":[72],"which":[73],"generates":[74],"an":[75],"map":[77,117,121,148,175],"low":[79],"overhead":[81,187],"while":[82],"utilizing":[83],"multi-level":[84],"feature":[85,116,120],"maps.":[86],"First,":[87],"Collection-CAM":[89,108,144,168],"searches":[90],"for":[91],"most":[93],"appropriate":[94],"form":[95],"partition":[98],"through":[99],"bottom-up":[100],"clustering":[101,103],"validation":[104],"process.":[105,150],"Then":[106],"applies":[109],"different":[110],"pre-processing":[111],"procedures":[112],"on":[113,153],"shallow":[115],"final":[119],"overcome":[123],"positiveness":[126],"when":[127],"applied":[128],"without":[129],"distinction.":[130],"By":[131],"combining":[132],"collection-wise":[133],"masks":[134],"according":[135],"contribution":[138],"confidence":[141],"score,":[142],"completes":[145],"generation":[149],"Experimental":[151],"results":[152],"ImageNet1k,":[154],"UC":[155],"Merced,":[156],"CUB":[158],"dataset":[159],"various":[161],"models":[164],"demonstrate":[165],"that":[166],"not":[169],"can":[171],"synthesize":[172],"a":[173,177],"better":[178],"visual":[179],"explanation":[180],"but":[181],"also":[182],"requires":[183],"significantly":[184],"lower":[185],"compared":[188],"region-based":[192],"methods.":[194]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
