{"id":"https://openalex.org/W4283376507","doi":"https://doi.org/10.1145/3512527.3531382","title":"Phrase-level Prediction for Video Temporal Localization","display_name":"Phrase-level Prediction for Video Temporal Localization","publication_year":2022,"publication_date":"2022-06-23","ids":{"openalex":"https://openalex.org/W4283376507","doi":"https://doi.org/10.1145/3512527.3531382"},"language":"en","primary_location":{"id":"doi:10.1145/3512527.3531382","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3512527.3531382","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 International Conference on Multimedia Retrieval","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/A5022183455","display_name":"Sizhe Li","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Sizhe Li","raw_affiliation_strings":["Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100429513","display_name":"Chang Li","orcid":"https://orcid.org/0000-0001-9588-8118"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chang Li","raw_affiliation_strings":["Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071596230","display_name":"Minghang Zheng","orcid":"https://orcid.org/0000-0003-1612-975X"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Minghang Zheng","raw_affiliation_strings":["Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100355884","display_name":"Yang Liu","orcid":"https://orcid.org/0000-0002-4259-3882"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]},{"id":"https://openalex.org/I4392738278","display_name":"Beijing Institute for General Artificial Intelligence","ror":"https://ror.org/02kw1ws04","country_code":null,"type":"facility","lineage":["https://openalex.org/I4392738278"]},{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Liu","raw_affiliation_strings":["Peking University &amp; Beijing Institute for General Artificial Intelligence, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Peking University &amp; Beijing Institute for General Artificial Intelligence, Beijing, China","institution_ids":["https://openalex.org/I4210100255","https://openalex.org/I20231570","https://openalex.org/I4392738278"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5022183455"],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":0.5033,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.62684731,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"360","last_page":"368"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","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/T11714","display_name":"Multimodal Machine Learning Applications","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/T10812","display_name":"Human Pose and Action Recognition","score":0.9965999722480774,"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/T10028","display_name":"Topic Modeling","score":0.9961000084877014,"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":0.814733624458313},{"id":"https://openalex.org/keywords/phrase","display_name":"Phrase","score":0.8055810928344727},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.787680447101593},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7154587507247925},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.6812742948532104},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5982826352119446},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4447246789932251},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.06897944211959839}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.814733624458313},{"id":"https://openalex.org/C2776224158","wikidata":"https://www.wikidata.org/wiki/Q187931","display_name":"Phrase","level":2,"score":0.8055810928344727},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.787680447101593},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7154587507247925},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.6812742948532104},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5982826352119446},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4447246789932251},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.06897944211959839},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3512527.3531382","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3512527.3531382","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.699999988079071,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W2563399268","https://openalex.org/W2963393391","https://openalex.org/W2964089981","https://openalex.org/W2979933490","https://openalex.org/W2997429269","https://openalex.org/W3035339529","https://openalex.org/W3093174808","https://openalex.org/W3095206982","https://openalex.org/W3104893896","https://openalex.org/W3120889656","https://openalex.org/W3121021526","https://openalex.org/W3174364033","https://openalex.org/W3175817778","https://openalex.org/W3176763654","https://openalex.org/W3178087530","https://openalex.org/W3180476551","https://openalex.org/W3199576129","https://openalex.org/W3210101297","https://openalex.org/W4312402470"],"related_works":["https://openalex.org/W2889818188","https://openalex.org/W1978971213","https://openalex.org/W1567338489","https://openalex.org/W2564816500","https://openalex.org/W38394648","https://openalex.org/W2119727789","https://openalex.org/W1586984800","https://openalex.org/W1597901428","https://openalex.org/W159132833","https://openalex.org/W4305026484"],"abstract_inverted_index":{"Video":[0],"temporal":[1,193],"localization":[2,37,148,194,201],"aims":[3],"to":[4,49,58,129,145,173],"locate":[5],"a":[6,11,16,102,142,169],"period":[7],"that":[8,23,64,105,125,175,185],"semantically":[9],"matches":[10],"natural":[12],"language":[13,89],"query":[14],"in":[15,74,155,199],"given":[17],"untrimmed":[18],"video.":[19],"We":[20],"empirically":[21],"observe":[22],"although":[24],"existing":[25,66],"approaches":[26],"gain":[27],"steady":[28],"progress":[29],"on":[30,122,131],"sentence":[31,71,110,200],"localization,":[32,113],"the":[33,44,65,70,75,82,92,123,159,204],"performance":[34,198],"of":[35,54,81,191],"phrase":[36,45,151],"is":[38,97,212],"far":[39],"from":[40],"satisfactory.":[41],"In":[42],"principle,":[43],"should":[46,138],"be":[47,59,179],"easier":[48],"localize":[50],"as":[51],"fewer":[52],"combinations":[53],"visual":[55,87],"concepts":[56],"need":[57],"considered;":[60],"such":[61],"incapability":[62],"indicates":[63],"models":[67],"only":[68],"capture":[69],"annotation":[72,153],"bias":[73],"benchmark":[76],"but":[77],"lack":[78],"sufficient":[79],"understanding":[80],"intrinsic":[83],"relationship":[84],"between":[85],"simple":[86],"and":[88,95,111,162,202,207],"concepts,":[90],"thus":[91],"model":[93],"generalization":[94,208],"interpretability":[96,206],"questioned.":[98],"This":[99],"paper":[100],"proposes":[101],"unified":[103],"framework":[104],"can":[106,178],"deal":[107],"with":[108],"both":[109],"phrase-level":[112,147,192],"namely":[114],"Phrase":[115],"Level":[116],"Prediction":[117],"Net":[118],"(PLPNet).":[119],"Specifically,":[120],"based":[121],"hypothesis":[124],"similar":[126,132],"phrases":[127],"tend":[128],"focus":[130],"video":[133],"cues,":[134],"while":[135,195],"dissimilar":[136],"ones":[137],"not,":[139],"we":[140,167],"build":[141],"contrastive":[143,176],"mechanism":[144],"restrain":[146],"without":[149],"fine-grained":[150],"boundary":[152],"required":[154],"training.":[156],"Moreover,":[157],"considering":[158],"sentence's":[160],"flexibility":[161],"wide":[163],"discrepancy":[164],"among":[165],"phrases,":[166],"propose":[168],"clustering-based":[170],"batch":[171],"sampler":[172],"ensure":[174],"learning":[177],"conducted":[180],"efficiently.":[181],"Extensive":[182],"experiments":[183],"demonstrate":[184],"our":[186],"method":[187],"surpasses":[188],"state-of-the-art":[189],"methods":[190],"maintaining":[196],"high":[197],"boosting":[203],"model's":[205],"capability.":[209],"Our":[210],"code":[211],"available":[213],"at":[214],"https://github.com/sizhelee/PLPNet.":[215]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
