{"id":"https://openalex.org/W3003088567","doi":"https://doi.org/10.1109/wacv45572.2020.9093620","title":"Weakly Supervised Temporal Action Localization Using Deep Metric Learning","display_name":"Weakly Supervised Temporal Action Localization Using Deep Metric Learning","publication_year":2020,"publication_date":"2020-03-01","ids":{"openalex":"https://openalex.org/W3003088567","doi":"https://doi.org/10.1109/wacv45572.2020.9093620","mag":"3003088567"},"language":"en","primary_location":{"id":"doi:10.1109/wacv45572.2020.9093620","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv45572.2020.9093620","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2001.07793","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5082493192","display_name":"Ashraful Islam","orcid":"https://orcid.org/0000-0003-2367-2013"},"institutions":[{"id":"https://openalex.org/I165799507","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94","country_code":"US","type":"education","lineage":["https://openalex.org/I165799507"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ashraful Islam","raw_affiliation_strings":["Rensselaer Polytechnic Institute","Rensselaer  Polytechnic Institute"],"affiliations":[{"raw_affiliation_string":"Rensselaer Polytechnic Institute","institution_ids":["https://openalex.org/I165799507"]},{"raw_affiliation_string":"Rensselaer  Polytechnic Institute","institution_ids":["https://openalex.org/I165799507"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049207791","display_name":"Richard J. Radke","orcid":"https://orcid.org/0000-0001-5064-7775"},"institutions":[{"id":"https://openalex.org/I165799507","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94","country_code":"US","type":"education","lineage":["https://openalex.org/I165799507"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Richard J. Radke","raw_affiliation_strings":["Rensselaer Polytechnic Institute","Rensselaer  Polytechnic Institute"],"affiliations":[{"raw_affiliation_string":"Rensselaer Polytechnic Institute","institution_ids":["https://openalex.org/I165799507"]},{"raw_affiliation_string":"Rensselaer  Polytechnic Institute","institution_ids":["https://openalex.org/I165799507"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5082493192"],"corresponding_institution_ids":["https://openalex.org/I165799507"],"apc_list":null,"apc_paid":null,"fwci":0.2944,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.54404481,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"536","last_page":"545"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","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/T10812","display_name":"Human Pose and Action Recognition","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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9954000115394592,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9815999865531921,"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":0.7621921300888062},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7376942038536072},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6973493099212646},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.5890482068061829},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.547900915145874},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5434834957122803},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.4747353494167328},{"id":"https://openalex.org/keywords/cross-entropy","display_name":"Cross entropy","score":0.46401986479759216},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4632170796394348},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.4616680443286896},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32254183292388916},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.19138064980506897},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.16632944345474243},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.08798021078109741}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7621921300888062},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7376942038536072},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6973493099212646},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.5890482068061829},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.547900915145874},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5434834957122803},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.4747353494167328},{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.46401986479759216},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4632170796394348},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.4616680443286896},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32254183292388916},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.19138064980506897},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.16632944345474243},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.08798021078109741},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/wacv45572.2020.9093620","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv45572.2020.9093620","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2001.07793","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2001.07793","pdf_url":"https://arxiv.org/pdf/2001.07793","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:3003088567","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2001.07793.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2001.07793","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2001.07793","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2001.07793","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2001.07793","pdf_url":"https://arxiv.org/pdf/2001.07793","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3003088567.pdf","grobid_xml":"https://content.openalex.org/works/W3003088567.grobid-xml"},"referenced_works_count":61,"referenced_works":["https://openalex.org/W24089286","https://openalex.org/W61928934","https://openalex.org/W845365781","https://openalex.org/W1522734439","https://openalex.org/W1927052826","https://openalex.org/W1994488211","https://openalex.org/W2021354639","https://openalex.org/W2062634420","https://openalex.org/W2076434944","https://openalex.org/W2096733369","https://openalex.org/W2106053110","https://openalex.org/W2119031011","https://openalex.org/W2126579184","https://openalex.org/W2133324800","https://openalex.org/W2136853139","https://openalex.org/W2137894166","https://openalex.org/W2138621090","https://openalex.org/W2156303437","https://openalex.org/W2157364932","https://openalex.org/W2235034809","https://openalex.org/W2337252826","https://openalex.org/W2342662179","https://openalex.org/W2477205648","https://openalex.org/W2535977253","https://openalex.org/W2552414813","https://openalex.org/W2585747585","https://openalex.org/W2593722617","https://openalex.org/W2600081845","https://openalex.org/W2604113307","https://openalex.org/W2606461407","https://openalex.org/W2618799552","https://openalex.org/W2619947201","https://openalex.org/W2884293275","https://openalex.org/W2895240652","https://openalex.org/W2900451465","https://openalex.org/W2948229620","https://openalex.org/W2950534130","https://openalex.org/W2950971447","https://openalex.org/W2962709777","https://openalex.org/W2962876901","https://openalex.org/W2963045696","https://openalex.org/W2963155035","https://openalex.org/W2963247196","https://openalex.org/W2963524571","https://openalex.org/W2963603913","https://openalex.org/W2964107628","https://openalex.org/W2964121744","https://openalex.org/W2964214371","https://openalex.org/W2964216549","https://openalex.org/W3099206234","https://openalex.org/W4253314985","https://openalex.org/W6600983433","https://openalex.org/W6602537450","https://openalex.org/W6606760634","https://openalex.org/W6631190155","https://openalex.org/W6675751002","https://openalex.org/W6682864246","https://openalex.org/W6703281212","https://openalex.org/W6724944384","https://openalex.org/W6736422646","https://openalex.org/W6955071965"],"related_works":["https://openalex.org/W3010504658","https://openalex.org/W2969782179","https://openalex.org/W3193800882","https://openalex.org/W2895240652","https://openalex.org/W3158492585","https://openalex.org/W2931495284","https://openalex.org/W2883412206","https://openalex.org/W3035524964","https://openalex.org/W2916649786","https://openalex.org/W3094484141","https://openalex.org/W3120833752","https://openalex.org/W2950534130","https://openalex.org/W3170310247","https://openalex.org/W2986718935","https://openalex.org/W3115390435","https://openalex.org/W2991396237","https://openalex.org/W2940924736","https://openalex.org/W3015104098","https://openalex.org/W2953309568","https://openalex.org/W3033595049"],"abstract_inverted_index":{"Temporal":[0],"action":[1,12,24,35,51,58,71,91],"localization":[2,13,52],"is":[3,28],"an":[4],"important":[5],"step":[6],"towards":[7],"video":[8,131],"understanding.":[9],"Most":[10],"current":[11,140],"methods":[14],"depend":[15],"on":[16,127],"untrimmed":[17,130],"videos":[18],"with":[19],"full":[20],"temporal":[21,38,50,121],"annotations":[22],"of":[23,40,115,117],"instances.":[25,92],"However,":[26],"it":[27],"expensive":[29],"and":[30,37,79,101,134,152],"time-consuming":[31],"to":[32,69,85],"annotate":[33],"both":[34,116],"labels":[36,72],"boundaries":[39],"videos.":[41],"To":[42],"this":[43],"end,":[44],"we":[45],"propose":[46,65],"a":[47,66,80,96,102,106],"weakly":[48],"supervised":[49],"method":[53],"that":[54],"only":[55],"requires":[56],"video-level":[57],"instances":[59],"as":[60],"supervision":[61],"during":[62],"training.":[63],"We":[64,93,123],"classification":[67],"module":[68,84],"generate":[70],"for":[73,143,156],"each":[74],"segment":[75],"in":[76,120],"the":[77,87,113,139],"video,":[78],"deep":[81],"metric":[82,103],"learning":[83],"learn":[86],"similarity":[88],"between":[89],"different":[90],"jointly":[94],"optimize":[95],"balanced":[97],"binary":[98],"cross-entropy":[99],"loss":[100,104],"using":[105],"standard":[107],"backpropagation":[108],"algorithm.":[109],"Extensive":[110],"experiments":[111],"demonstrate":[112],"effectiveness":[114],"these":[118],"components":[119],"localization.":[122],"evaluate":[124],"our":[125],"algorithm":[126],"two":[128],"challenging":[129],"datasets:":[132],"THUMOS14":[133,144],"ActivityNet1.2.":[135,157],"Our":[136],"approach":[137],"improves":[138],"state-of-the-art":[141],"result":[142],"by":[145],"6.5%":[146],"mAP":[147],"at":[148],"IoU":[149],"threshold":[150],"0.5,":[151],"achieves":[153],"competitive":[154],"performance":[155]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}