{"id":"https://openalex.org/W4301963599","doi":"https://doi.org/10.1145/3240508.3240511","title":"Step-by-step Erasion, One-by-one Collection","display_name":"Step-by-step Erasion, One-by-one Collection","publication_year":2018,"publication_date":"2018-10-15","ids":{"openalex":"https://openalex.org/W4301963599","doi":"https://doi.org/10.1145/3240508.3240511"},"language":"en","primary_location":{"id":"doi:10.1145/3240508.3240511","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3240508.3240511","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th ACM international conference on Multimedia","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/A5015772076","display_name":"Jia-Xing Zhong","orcid":null},"institutions":[{"id":"https://openalex.org/I4210128628","display_name":"Peking University Shenzhen Hospital","ror":"https://ror.org/03kkjyb15","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210128628"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jia-Xing Zhong","raw_affiliation_strings":["Peking University, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University, Shenzhen, China","institution_ids":["https://openalex.org/I4210128628"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100419530","display_name":"Nannan Li","orcid":"https://orcid.org/0000-0003-3688-6766"},"institutions":[{"id":"https://openalex.org/I4210128628","display_name":"Peking University Shenzhen Hospital","ror":"https://ror.org/03kkjyb15","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210128628"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Nannan Li","raw_affiliation_strings":["Peking University, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University, Shenzhen, China","institution_ids":["https://openalex.org/I4210128628"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109153417","display_name":"Weijie Kong","orcid":"https://orcid.org/0000-0003-1700-4801"},"institutions":[{"id":"https://openalex.org/I4210128628","display_name":"Peking University Shenzhen Hospital","ror":"https://ror.org/03kkjyb15","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210128628"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weijie Kong","raw_affiliation_strings":["Peking University, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University, Shenzhen, China","institution_ids":["https://openalex.org/I4210128628"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040210900","display_name":"Tao Zhang","orcid":"https://orcid.org/0000-0003-3966-4691"},"institutions":[{"id":"https://openalex.org/I4210128628","display_name":"Peking University Shenzhen Hospital","ror":"https://ror.org/03kkjyb15","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210128628"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Zhang","raw_affiliation_strings":["Peking University, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University, Shenzhen, China","institution_ids":["https://openalex.org/I4210128628"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019720274","display_name":"Thomas H. Li","orcid":null},"institutions":[{"id":"https://openalex.org/I4210128628","display_name":"Peking University Shenzhen Hospital","ror":"https://ror.org/03kkjyb15","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210128628"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Thomas H. Li","raw_affiliation_strings":["Peking University, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University, Shenzhen, China","institution_ids":["https://openalex.org/I4210128628"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100447691","display_name":"Ge Li","orcid":"https://orcid.org/0000-0003-0140-0949"},"institutions":[{"id":"https://openalex.org/I4210128628","display_name":"Peking University Shenzhen Hospital","ror":"https://ror.org/03kkjyb15","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210128628"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ge Li","raw_affiliation_strings":["Peking University, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University, Shenzhen, China","institution_ids":["https://openalex.org/I4210128628"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.0741,"has_fulltext":false,"cited_by_count":93,"citation_normalized_percentile":{"value":0.94101079,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"35","last_page":"44"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","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"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9815000295639038,"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/computer-science","display_name":"Computer science","score":0.7882277965545654},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7245845198631287},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6962875127792358},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6906497478485107},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.6668719053268433},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5123246908187866},{"id":"https://openalex.org/keywords/conditional-random-field","display_name":"Conditional random field","score":0.509171187877655},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44793522357940674},{"id":"https://openalex.org/keywords/snippet","display_name":"Snippet","score":0.4140709638595581},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.12065020203590393}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7882277965545654},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7245845198631287},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6962875127792358},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6906497478485107},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.6668719053268433},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5123246908187866},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.509171187877655},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44793522357940674},{"id":"https://openalex.org/C2777822670","wikidata":"https://www.wikidata.org/wiki/Q1120538","display_name":"Snippet","level":2,"score":0.4140709638595581},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.12065020203590393},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3240508.3240511","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3240508.3240511","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th ACM international conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W262578090","https://openalex.org/W1522734439","https://openalex.org/W1944615693","https://openalex.org/W2016053056","https://openalex.org/W2033740597","https://openalex.org/W2105101328","https://openalex.org/W2145038566","https://openalex.org/W2146444479","https://openalex.org/W2155893237","https://openalex.org/W2163292664","https://openalex.org/W2175640409","https://openalex.org/W2342662179","https://openalex.org/W2463824207","https://openalex.org/W2508429489","https://openalex.org/W2530494944","https://openalex.org/W2593722617","https://openalex.org/W2607566495","https://openalex.org/W2766402183","https://openalex.org/W2766712264","https://openalex.org/W2963045696","https://openalex.org/W2963247196","https://openalex.org/W2963321993","https://openalex.org/W2963524571","https://openalex.org/W2963820951","https://openalex.org/W2963934593","https://openalex.org/W2964214371","https://openalex.org/W2990138404","https://openalex.org/W3100481960","https://openalex.org/W3103081334","https://openalex.org/W4245551996","https://openalex.org/W4301045096","https://openalex.org/W4323697410"],"related_works":["https://openalex.org/W1607713096","https://openalex.org/W3036724449","https://openalex.org/W2798329462","https://openalex.org/W1511521437","https://openalex.org/W3201315194","https://openalex.org/W2163278254","https://openalex.org/W155708904","https://openalex.org/W1574213390","https://openalex.org/W4308706875","https://openalex.org/W3056617467"],"abstract_inverted_index":{"Weakly":[0],"supervised":[1,34,175,188],"temporal":[2,152,176],"action":[3,78,177],"detection":[4,124,178],"is":[5,22,43,72,121,147],"a":[6,52,70,91,95,109,141,185],"Herculean":[7],"task":[8],"in":[9,54,137],"understanding":[10],"untrimmed":[11],"videos,":[12],"since":[13],"no":[14],"supervisory":[15],"signal":[16],"except":[17],"the":[18,28,76,113,127,138,151],"video-level":[19],"category":[20,31],"label":[21],"available":[23],"on":[24,159],"training":[25],"data.":[26],"Under":[27],"supervision":[29],"of":[30,56,97],"labels,":[32],"weakly":[33,174],"detectors":[35],"are":[36,66],"usually":[37],"built":[38],"upon":[39],"classifiers.":[40],"However,":[41],"there":[42],"an":[44],"inherent":[45],"contradiction":[46],"between":[47],"classifier":[48,53],"and":[49,164,180],"detector;":[50],"i.e.,":[51],"pursuit":[55],"high":[57],"classification":[58],"performance":[59],"prefers":[60],"top-level":[61],"discriminative":[62],"video":[63],"clips":[64,103],"that":[65,169],"extremely":[67],"fragmentary,":[68],"whereas":[69],"detector":[71,92,171],"obliged":[73],"to":[74,99,119,122,149],"discover":[75],"whole":[77],"instance":[79],"without":[80],"missing":[81],"any":[82],"relevant":[83],"snippet.":[84],"To":[85,135],"reconcile":[86],"this":[87],"contradiction,":[88],"we":[89,117],"train":[90],"by":[93],"driving":[94],"series":[96],"classifiers":[98,130],"find":[100],"new":[101],"actionness":[102],"progressively,":[104],"via":[105],"step-by-step":[106],"erasion":[107],"from":[108,126],"complete":[110],"video.":[111],"During":[112],"test":[114],"phase,":[115],"all":[116],"need":[118],"do":[120],"collect":[123],"results":[125],"one-by-one":[128],"trained":[129],"at":[131],"various":[132],"erasing":[133],"steps.":[134],"assist":[136],"collection":[139],"process,":[140],"fully":[142],"connected":[143],"conditional":[144],"random":[145],"field":[146],"established":[148],"refine":[150],"localization":[153],"outputs.":[154],"We":[155],"evaluate":[156],"our":[157,170],"approach":[158],"two":[160],"prevailing":[161],"datasets,":[162],"THUMOS'14":[163],"ActivityNet.":[165],"The":[166],"experiments":[167],"show":[168],"advances":[172],"state-of-the-art":[173],"results,":[179],"even":[181],"compares":[182],"with":[183],"quite":[184],"few":[186],"strongly":[187],"methods.":[189]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":22},{"year":2022,"cited_by_count":23},{"year":2021,"cited_by_count":20},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}