{"id":"https://openalex.org/W4304080820","doi":"https://doi.org/10.1145/3503161.3548324","title":"Dynamic Scene Graph Generation via Temporal Prior Inference","display_name":"Dynamic Scene Graph Generation via Temporal Prior Inference","publication_year":2022,"publication_date":"2022-10-10","ids":{"openalex":"https://openalex.org/W4304080820","doi":"https://doi.org/10.1145/3503161.3548324"},"language":"en","primary_location":{"id":"doi:10.1145/3503161.3548324","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3503161.3548324","pdf_url":null,"source":{"id":"https://openalex.org/S4363608757","display_name":"Proceedings of the 30th ACM International Conference on Multimedia","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Multimedia","raw_type":"proceedings-article"},"type":"conference-paper","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/A5101847675","display_name":"Shuang Wang","orcid":"https://orcid.org/0000-0003-2224-5108"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuang Wang","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066645546","display_name":"Lianli Gao","orcid":"https://orcid.org/0000-0002-2522-6394"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lianli Gao","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090700061","display_name":"Xinyu Lyu","orcid":"https://orcid.org/0000-0003-2479-8881"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinyu Lyu","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103213404","display_name":"Yuyu Guo","orcid":"https://orcid.org/0000-0003-4376-6922"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuyu Guo","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087623065","display_name":"Pengpeng Zeng","orcid":"https://orcid.org/0000-0002-0672-3790"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pengpeng Zeng","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036987388","display_name":"Jingkuan Song","orcid":"https://orcid.org/0000-0002-2549-8322"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]},{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingkuan Song","raw_affiliation_strings":["University of Electronic Science and Technology of China &amp; Peng Cheng Laboratory, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China &amp; Peng Cheng Laboratory, Chengdu, China","institution_ids":["https://openalex.org/I4210136793","https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":27,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5793","last_page":"5801"},"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":0.9998000264167786,"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/T11439","display_name":"Video Analysis and Summarization","score":0.9896000027656555,"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/inference","display_name":"Inference","score":0.906622052192688},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7777715921401978},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6114073395729065},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5055556297302246},{"id":"https://openalex.org/keywords/temporal-database","display_name":"Temporal database","score":0.4695575535297394},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4633169174194336},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3485206365585327},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.24623268842697144},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.18126025795936584}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.906622052192688},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7777715921401978},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6114073395729065},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5055556297302246},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.4695575535297394},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4633169174194336},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3485206365585327},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.24623268842697144},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.18126025795936584}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3503161.3548324","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3503161.3548324","pdf_url":null,"source":{"id":"https://openalex.org/S4363608757","display_name":"Proceedings of the 30th ACM International Conference on Multimedia","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5099999904632568,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[{"id":"https://openalex.org/G8102878355","display_name":null,"funder_award_id":"Grant No. 62020106008, No. 62122018, No. 61772116, No. 61872064","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2077069816","https://openalex.org/W2479423890","https://openalex.org/W2591644541","https://openalex.org/W2765137706","https://openalex.org/W2807697862","https://openalex.org/W2886970679","https://openalex.org/W2963184176","https://openalex.org/W2963514444","https://openalex.org/W2963524571","https://openalex.org/W2963536419","https://openalex.org/W2963938081","https://openalex.org/W2987919422","https://openalex.org/W2990503944","https://openalex.org/W3034679267","https://openalex.org/W3108864070","https://openalex.org/W3186621246","https://openalex.org/W3193902142","https://openalex.org/W3207608362","https://openalex.org/W3207659901","https://openalex.org/W4214607465","https://openalex.org/W4221148458","https://openalex.org/W4285602612"],"related_works":["https://openalex.org/W2055243143","https://openalex.org/W4321636575","https://openalex.org/W1986418932","https://openalex.org/W2357796999","https://openalex.org/W2045526782","https://openalex.org/W2741131631","https://openalex.org/W2156919374","https://openalex.org/W1984019423","https://openalex.org/W2961085424","https://openalex.org/W4280588203"],"abstract_inverted_index":{"Real-world":[0],"videos":[1,83],"are":[2,93],"composed":[3],"of":[4,54,76,82,144,176],"complex":[5],"actions":[6],"with":[7,134,208],"inherent":[8,91],"temporal":[9,29,52,68,152],"continuity":[10,30,69,163],"(eg":[11],"\"person-touching-bottle\"":[12],"is":[13],"usually":[14],"followed":[15],"by":[16,57,70,159,206,218],"\"person-holding-bottle\").":[17],"In":[18,197],"this":[19],"work,":[20],"we":[21,60,149],"propose":[22],"a":[23,80,156],"novel":[24],"method":[25],"to":[26,44,65,139,216],"mine":[27],"such":[28],"for":[31,105,189],"dynamic":[32,146],"scene":[33,147],"graph":[34],"generation":[35],"(DSGG),":[36],"namely":[37],"Temporal":[38,96,130],"Prior":[39,97,131],"Inference":[40,132],"(TPI).":[41],"As":[42],"opposed":[43],"current":[45,118],"DSGG":[46,157],"methods,":[47],"which":[48,100],"individually":[49],"capture":[50],"the":[51,62,67,72,112,128,142,151,174,181,199],"dependence":[53],"each":[55],"video":[56],"refining":[58],"representations,":[59],"make":[61],"first":[63],"attempt":[64],"explore":[66],"extracting":[71],"entire":[73],"co-occurrence":[74],"patterns":[75,92],"action":[77],"categories":[78],"from":[79,124],"variety":[81],"in":[84,117],"Action":[85,182],"Genome":[86,183],"(AG)":[87],"dataset.":[88],"Then,":[89],"these":[90],"organized":[94],"as":[95,102],"Knowledge":[98],"(TPK)":[99],"serves":[101],"prior":[103,113,153],"knowledge":[104],"models'":[106],"learning":[107],"and":[108,165,187,192],"inference.":[109,167,221],"Furthermore,":[110],"given":[111],"knowledge,":[114],"human-object":[115],"relationships":[116],"frames":[119,126],"can":[120,202],"be":[121,203],"effectively":[122],"inferred":[123],"adjacent":[125],"via":[127],"robust":[129],"algorithm":[133],"tiny":[135],"computation":[136],"cost.":[137],"Specifically,":[138],"efficiently":[140],"guide":[141],"generating":[143],"temporal-consistent":[145],"graphs,":[148],"incorporate":[150],"inference":[154,200],"into":[155],"framework":[158],"introducing":[160],"frame":[161],"enhancement,":[162],"loss,":[164],"fast":[166,220],"The":[168],"proposed":[169],"model-agnostic":[170],"strategies":[171],"significantly":[172,204],"boost":[173],"performances":[175],"existing":[177],"state-of-the-art":[178],"models":[179],"on":[180,195,212],"dataset,":[184],"achieving":[185],"69.7":[186],"72.6":[188],"[email":[190,193,213],"protected]":[191,194,214],"PredCLS.":[196],"addition,":[198],"speed":[201],"reduced":[205],"41%":[207],"an":[209],"acceptable":[210],"drop":[211],"(69.7":[215],"66.8)":[217],"utilizing":[219]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":5}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
