{"id":"https://openalex.org/W2006017062","doi":"https://doi.org/10.1145/2502081.2502191","title":"Semantic pooling for complex event detection","display_name":"Semantic pooling for complex event detection","publication_year":2013,"publication_date":"2013-10-21","ids":{"openalex":"https://openalex.org/W2006017062","doi":"https://doi.org/10.1145/2502081.2502191","mag":"2006017062"},"language":"en","primary_location":{"id":"doi:10.1145/2502081.2502191","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2502081.2502191","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 21st 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/A5101600341","display_name":"Qian Yu","orcid":"https://orcid.org/0000-0002-0538-7940"},"institutions":[{"id":"https://openalex.org/I1298353152","display_name":"SRI International","ror":"https://ror.org/05s570m15","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I1298353152"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Qian Yu","raw_affiliation_strings":["SRI International, Princeton, NJ, USA"],"affiliations":[{"raw_affiliation_string":"SRI International, Princeton, NJ, USA","institution_ids":["https://openalex.org/I1298353152"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032552506","display_name":"Jingen Liu","orcid":"https://orcid.org/0000-0003-3133-3644"},"institutions":[{"id":"https://openalex.org/I1298353152","display_name":"SRI International","ror":"https://ror.org/05s570m15","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I1298353152"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jingen Liu","raw_affiliation_strings":["SRI International, Princeton, NJ, USA"],"affiliations":[{"raw_affiliation_string":"SRI International, Princeton, NJ, USA","institution_ids":["https://openalex.org/I1298353152"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101409148","display_name":"Hui Cheng","orcid":"https://orcid.org/0000-0003-2579-7004"},"institutions":[{"id":"https://openalex.org/I1298353152","display_name":"SRI International","ror":"https://ror.org/05s570m15","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I1298353152"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hui Cheng","raw_affiliation_strings":["SRI International, Princeton, NJ, USA"],"affiliations":[{"raw_affiliation_string":"SRI International, Princeton, NJ, USA","institution_ids":["https://openalex.org/I1298353152"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028124265","display_name":"Ajay Divakaran","orcid":"https://orcid.org/0000-0003-0371-5346"},"institutions":[{"id":"https://openalex.org/I1298353152","display_name":"SRI International","ror":"https://ror.org/05s570m15","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I1298353152"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ajay Divakaran","raw_affiliation_strings":["SRI International, Princeton, NJ, USA"],"affiliations":[{"raw_affiliation_string":"SRI International, Princeton, NJ, USA","institution_ids":["https://openalex.org/I1298353152"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091076182","display_name":"Harpreet Sawhney","orcid":null},"institutions":[{"id":"https://openalex.org/I1298353152","display_name":"SRI International","ror":"https://ror.org/05s570m15","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I1298353152"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Harpreet Sawhney","raw_affiliation_strings":["SRI International, Princeton, NJ, USA"],"affiliations":[{"raw_affiliation_string":"SRI International, Princeton, NJ, USA","institution_ids":["https://openalex.org/I1298353152"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101600341"],"corresponding_institution_ids":["https://openalex.org/I1298353152"],"apc_list":null,"apc_paid":null,"fwci":1.0886,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.80011983,"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":"733","last_page":"736"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","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/T10812","display_name":"Human Pose and Action Recognition","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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9995999932289124,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9993000030517578,"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/pooling","display_name":"Pooling","score":0.9446060657501221},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.814159631729126},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6953014135360718},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6831138134002686},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.6774325370788574},{"id":"https://openalex.org/keywords/semantic-feature","display_name":"Semantic feature","score":0.5909894704818726},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.508391797542572},{"id":"https://openalex.org/keywords/semantic-gap","display_name":"Semantic gap","score":0.4484293460845947},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4408688545227051},{"id":"https://openalex.org/keywords/semantic-computing","display_name":"Semantic computing","score":0.4359681308269501},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.41927778720855713},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4080512225627899},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3897749185562134},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3863082230091095},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3341655731201172},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.19682878255844116},{"id":"https://openalex.org/keywords/image-retrieval","display_name":"Image retrieval","score":0.11931470036506653},{"id":"https://openalex.org/keywords/semantic-web","display_name":"Semantic Web","score":0.10215404629707336}],"concepts":[{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.9446060657501221},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.814159631729126},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6953014135360718},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6831138134002686},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.6774325370788574},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.5909894704818726},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.508391797542572},{"id":"https://openalex.org/C86034646","wikidata":"https://www.wikidata.org/wiki/Q474311","display_name":"Semantic gap","level":4,"score":0.4484293460845947},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4408688545227051},{"id":"https://openalex.org/C511149849","wikidata":"https://www.wikidata.org/wiki/Q7449051","display_name":"Semantic computing","level":3,"score":0.4359681308269501},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.41927778720855713},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4080512225627899},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3897749185562134},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3863082230091095},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3341655731201172},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.19682878255844116},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.11931470036506653},{"id":"https://openalex.org/C2129575","wikidata":"https://www.wikidata.org/wiki/Q54837","display_name":"Semantic Web","level":2,"score":0.10215404629707336},{"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2502081.2502191","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2502081.2502191","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 21st ACM international conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.75,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W195163846","https://openalex.org/W2002657139","https://openalex.org/W2018668305","https://openalex.org/W2020163092","https://openalex.org/W2020836902","https://openalex.org/W2027922120","https://openalex.org/W2061685007","https://openalex.org/W2126574503","https://openalex.org/W2141939040","https://openalex.org/W2147238549","https://openalex.org/W2151103935","https://openalex.org/W2162915993","https://openalex.org/W2290542723"],"related_works":["https://openalex.org/W1971623867","https://openalex.org/W2546942002","https://openalex.org/W1982770690","https://openalex.org/W2973523586","https://openalex.org/W4313591539","https://openalex.org/W3142532214","https://openalex.org/W2352132852","https://openalex.org/W1557944360","https://openalex.org/W2056070602","https://openalex.org/W2353314619"],"abstract_inverted_index":{"Complex":[0],"event":[1,138],"detection":[2],"is":[3,50],"very":[4,16],"challenging":[5],"in":[6],"open":[7],"source":[8],"such":[9],"as":[10],"You-Tube":[11],"videos,":[12],"which":[13,49],"usually":[14],"comprise":[15],"diverse":[17],"visual":[18,99],"contents":[19],"involving":[20],"various":[21],"object,":[22],"scene":[23],"and":[24,149],"action":[25],"concepts.":[26],"Not":[27],"all":[28],"of":[29,46,77],"them,":[30],"however,":[31],"are":[32],"relevant":[33],"to":[34,61,85,116],"the":[35,66,70,111,150,158],"event.":[36],"In":[37],"other":[38],"words,":[39],"a":[40,44,57,78,82,127],"video":[41,72],"may":[42],"contain":[43],"lot":[45],"\"junk\"":[47],"information":[48],"harmful":[51],"for":[52,90,130,134],"recognition.":[53,139],"Hence,":[54],"we":[55,80,95],"propose":[56],"semantic":[58,88,102,118,122,132,154],"pooling":[59,68,123,155,163],"approach":[60,84,143],"tackle":[62],"this":[63,93],"issue.":[64],"Unlike":[65],"conventional":[67,162],"over":[69],"entire":[71],"or":[73],"specific":[74],"spatial":[75],"regions":[76],"video,":[79],"employ":[81],"discriminative":[83],"acquire":[86],"abstract":[87],"\"regions\"":[89],"pooling.":[91],"For":[92],"purpose,":[94],"first":[96],"associate":[97],"low-level":[98,112,135],"words":[100],"with":[101,161],"concepts":[103,133],"via":[104],"their":[105,117],"co-occurrence":[106],"relationship.":[107],"We":[108,140],"then":[109],"pool":[110],"features":[113],"separately":[114],"according":[115],"information.":[119],"The":[120],"proposed":[121],"strategy":[124],"also":[125],"provides":[126],"new":[128],"mechanism":[129],"incorporating":[131],"feature":[136],"based":[137],"evaluate":[141],"our":[142],"on":[144],"TRECVID":[145],"MED":[146],"[1]":[147],"dataset":[148],"results":[151],"show":[152],"that":[153],"consistently":[156],"improves":[157],"performance":[159],"compared":[160],"strategies.":[164]},"counts_by_year":[{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":3},{"year":2014,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
