{"id":"https://openalex.org/W3115256143","doi":"https://doi.org/10.1109/vcip49819.2020.9301824","title":"Temporal Action Proposal Generation via Multi-Task Feature Learning","display_name":"Temporal Action Proposal Generation via Multi-Task Feature Learning","publication_year":2020,"publication_date":"2020-12-01","ids":{"openalex":"https://openalex.org/W3115256143","doi":"https://doi.org/10.1109/vcip49819.2020.9301824","mag":"3115256143"},"language":"en","primary_location":{"id":"doi:10.1109/vcip49819.2020.9301824","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip49819.2020.9301824","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Visual Communications and Image Processing (VCIP)","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/A5036556540","display_name":"Handong Ma","orcid":"https://orcid.org/0000-0001-9702-249X"},"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":"Handong Ma","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/A5080093489","display_name":"Lixin Duan","orcid":"https://orcid.org/0000-0002-0723-4016"},"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":"Lixin Duan","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"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I150229711"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.17177983,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"399","last_page":"402"},"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":0.9990000128746033,"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":0.993399977684021,"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":0.8165839910507202},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6953095197677612},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6395524740219116},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.6135640144348145},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5802419185638428},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5635520219802856},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5599363446235657},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.5541502833366394},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5363764762878418},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5282299518585205},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.5080723762512207},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.44842061400413513},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3869367241859436}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8165839910507202},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6953095197677612},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6395524740219116},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.6135640144348145},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5802419185638428},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5635520219802856},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5599363446235657},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.5541502833366394},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5363764762878418},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5282299518585205},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.5080723762512207},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.44842061400413513},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3869367241859436},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","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/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","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/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vcip49819.2020.9301824","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip49819.2020.9301824","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Visual Communications and Image Processing (VCIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1522734439","https://openalex.org/W1927052826","https://openalex.org/W2105101328","https://openalex.org/W2339391301","https://openalex.org/W2342662179","https://openalex.org/W2394849137","https://openalex.org/W2507009361","https://openalex.org/W2597958930","https://openalex.org/W2604114396","https://openalex.org/W2743691986","https://openalex.org/W2755876276","https://openalex.org/W2805042136","https://openalex.org/W2884969173","https://openalex.org/W2949436914","https://openalex.org/W2952435096","https://openalex.org/W2953229046","https://openalex.org/W2962677524","https://openalex.org/W2963448607","https://openalex.org/W2963829773","https://openalex.org/W2963877604","https://openalex.org/W2964008341","https://openalex.org/W2964107628","https://openalex.org/W2964214371","https://openalex.org/W2964274041","https://openalex.org/W2964279831","https://openalex.org/W2983918066","https://openalex.org/W2987913071","https://openalex.org/W2990503944","https://openalex.org/W2997706915","https://openalex.org/W3002181557","https://openalex.org/W4288860281","https://openalex.org/W6724944384","https://openalex.org/W6736356174","https://openalex.org/W6751389191","https://openalex.org/W6753943344","https://openalex.org/W6765710127","https://openalex.org/W6770023436"],"related_works":["https://openalex.org/W2348909947","https://openalex.org/W4292672442","https://openalex.org/W2362101859","https://openalex.org/W2791431590","https://openalex.org/W2905271011","https://openalex.org/W3164948662","https://openalex.org/W4289536128","https://openalex.org/W3153597579","https://openalex.org/W4298151006","https://openalex.org/W4309346246"],"abstract_inverted_index":{"Temporal":[0],"action":[1,27,96],"proposal":[2],"generation":[3],"is":[4],"an":[5,152],"active":[6],"topic":[7],"in":[8,37,116,156,187],"computer":[9],"vision":[10],"and":[11,32,56,113,127,190],"image/video":[12],"processing":[13],"communities,":[14],"which":[15,49],"aims":[16],"to":[17,24,51,67,106,123,137,158],"predict":[18],"a":[19,78,90,117,125,134],"set":[20],"of":[21,75,162],"temporal":[22,57,95,114],"proposals":[23,97],"discover":[25],"all":[26],"instances":[28],"with":[29,142,183],"high":[30,143],"recall":[31],"intersection":[33],"over":[34],"union":[35],"(IoU)":[36],"real-world":[38],"untrimmed":[39],"videos.":[40],"Many":[41],"previous":[42],"approaches":[43],"rely":[44],"on":[45,99,168],"the":[46,69,72,83,111,139,160,169,175,179,184,188,191],"two-stream":[47],"network":[48],"intends":[50],"simultaneously":[52],"extract":[53],"spatial":[54,112],"(appearance)":[55],"features":[58,115],"for":[59,81,93],"representing":[60],"each":[61],"video.":[62],"However,":[63],"few":[64],"work":[65],"considers":[66],"capture":[68],"correlations":[70],"between":[71,110],"two":[73],"kinds":[74],"features,":[76],"leaving":[77],"large":[79],"room":[80],"improving":[82],"model.":[84],"In":[85],"this":[86],"paper,":[87],"we":[88,104,132,150],"present":[89],"new":[91],"method":[92,177],"generating":[94],"based":[98],"multi-task":[100,118],"feature":[101,129],"learning.":[102],"Specifically,":[103],"aim":[105],"learn":[107],"shared":[108],"representation":[109],"learning":[119,154],"framework,":[120],"so":[121],"as":[122],"acquire":[124],"compact":[126],"precise":[128],"representation.":[130],"Moreover,":[131],"devise":[133],"correlation":[135],"loss":[136],"address":[138],"`weak-correlation'":[140],"problem":[141],"IoUs":[144],"but":[145],"low":[146],"confidences":[147],"cores.":[148],"Finally,":[149],"take":[151],"ensemble":[153],"strategy":[155],"order":[157],"inherit":[159],"advantages":[161],"existing":[163],"works.":[164],"Extensive":[165],"experimental":[166],"results":[167],"ActivityNet-1.3":[170],"challenge":[171],"dataset":[172],"show":[173],"that":[174],"proposed":[176],"achieves":[178],"best":[180],"performance,":[181],"compared":[182],"state-of-the-arts":[185],"reported":[186],"literature":[189],"official":[192],"leaderboard.":[193],"Our":[194],"code":[195],"will":[196],"be":[197],"released":[198],"soon.":[199]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}