When Did It Happen? Duration-informed Temporal Localization of Narrated Actions in Vlogs

We consider the task of temporal human action localization in lifestyle vlogs. We introduce a novel dataset consisting of manual annotations of temporal localization for 13,000 narrated actions in 1,200 video clips. We present an extensive analysis of this data, which allows us to better understand...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:ACM transactions on multimedia computing communications and applications 2022-11, Vol.18 (3s), p.1-18, Article 142
Hauptverfasser: Ignat, Oana, Castro, Santiago, Zhou, Yuhang, Bao, Jiajun, Shan, Dandan, Mihalcea, Rada
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 18
container_issue 3s
container_start_page 1
container_title ACM transactions on multimedia computing communications and applications
container_volume 18
creator Ignat, Oana
Castro, Santiago
Zhou, Yuhang
Bao, Jiajun
Shan, Dandan
Mihalcea, Rada
description We consider the task of temporal human action localization in lifestyle vlogs. We introduce a novel dataset consisting of manual annotations of temporal localization for 13,000 narrated actions in 1,200 video clips. We present an extensive analysis of this data, which allows us to better understand how the language and visual modalities interact throughout the videos. We propose a simple yet effective method to localize the narrated actions based on their expected duration. Through several experiments and analyses, we show that our method brings complementary information with respect to previous methods, and leads to improvements over previous work for the task of temporal action localization.
doi_str_mv 10.1145/3495211
format Article
fullrecord <record><control><sourceid>acm_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1145_3495211</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3495211</sourcerecordid><originalsourceid>FETCH-LOGICAL-a244t-38d9dd1398a230c85ecb040989fb985ddd8c75388c6b16c07dc9438e38eb3d5b3</originalsourceid><addsrcrecordid>eNo9kD1PwzAYhC0EEqUgdiZvTAY7_og9oaottFIES_nYIsd2wCiJIzsM8OtJaal00nu6e_QOB8AlwTeEMH5LmeIZIUdgQjgnSEjBjw-e56fgLKVPjKngTEzA2-uH6-DCW7ge4Er3vevu4OIr6sGHDvmuDrF1Fm5c24eoG1gEoxv_81fDUMNHHUd2JGZmGyXoO_jShPd0Dk5q3SR3sb9T8Hy_3MxXqHh6WM9nBdIZYwOi0iprCVVSZxQbyZ2pMMNKqrpSkltrpck5ldKIigiDc2sUo9KNqqjlFZ2C691fE0NK0dVlH32r43dJcLkdpNwPMpJXO1Kb9gD9l7_eoVqD</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>When Did It Happen? Duration-informed Temporal Localization of Narrated Actions in Vlogs</title><source>ACM Digital Library Complete</source><creator>Ignat, Oana ; Castro, Santiago ; Zhou, Yuhang ; Bao, Jiajun ; Shan, Dandan ; Mihalcea, Rada</creator><creatorcontrib>Ignat, Oana ; Castro, Santiago ; Zhou, Yuhang ; Bao, Jiajun ; Shan, Dandan ; Mihalcea, Rada</creatorcontrib><description>We consider the task of temporal human action localization in lifestyle vlogs. We introduce a novel dataset consisting of manual annotations of temporal localization for 13,000 narrated actions in 1,200 video clips. We present an extensive analysis of this data, which allows us to better understand how the language and visual modalities interact throughout the videos. We propose a simple yet effective method to localize the narrated actions based on their expected duration. Through several experiments and analyses, we show that our method brings complementary information with respect to previous methods, and leads to improvements over previous work for the task of temporal action localization.</description><identifier>ISSN: 1551-6857</identifier><identifier>EISSN: 1551-6865</identifier><identifier>DOI: 10.1145/3495211</identifier><language>eng</language><publisher>New York, NY: ACM</publisher><subject>Information systems ; Multimedia streaming</subject><ispartof>ACM transactions on multimedia computing communications and applications, 2022-11, Vol.18 (3s), p.1-18, Article 142</ispartof><rights>Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a244t-38d9dd1398a230c85ecb040989fb985ddd8c75388c6b16c07dc9438e38eb3d5b3</citedby><cites>FETCH-LOGICAL-a244t-38d9dd1398a230c85ecb040989fb985ddd8c75388c6b16c07dc9438e38eb3d5b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://dl.acm.org/doi/pdf/10.1145/3495211$$EPDF$$P50$$Gacm$$H</linktopdf><link.rule.ids>314,780,784,2280,27923,27924,40195,75999</link.rule.ids></links><search><creatorcontrib>Ignat, Oana</creatorcontrib><creatorcontrib>Castro, Santiago</creatorcontrib><creatorcontrib>Zhou, Yuhang</creatorcontrib><creatorcontrib>Bao, Jiajun</creatorcontrib><creatorcontrib>Shan, Dandan</creatorcontrib><creatorcontrib>Mihalcea, Rada</creatorcontrib><title>When Did It Happen? Duration-informed Temporal Localization of Narrated Actions in Vlogs</title><title>ACM transactions on multimedia computing communications and applications</title><addtitle>ACM TOMM</addtitle><description>We consider the task of temporal human action localization in lifestyle vlogs. We introduce a novel dataset consisting of manual annotations of temporal localization for 13,000 narrated actions in 1,200 video clips. We present an extensive analysis of this data, which allows us to better understand how the language and visual modalities interact throughout the videos. We propose a simple yet effective method to localize the narrated actions based on their expected duration. Through several experiments and analyses, we show that our method brings complementary information with respect to previous methods, and leads to improvements over previous work for the task of temporal action localization.</description><subject>Information systems</subject><subject>Multimedia streaming</subject><issn>1551-6857</issn><issn>1551-6865</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNo9kD1PwzAYhC0EEqUgdiZvTAY7_og9oaottFIES_nYIsd2wCiJIzsM8OtJaal00nu6e_QOB8AlwTeEMH5LmeIZIUdgQjgnSEjBjw-e56fgLKVPjKngTEzA2-uH6-DCW7ge4Er3vevu4OIr6sGHDvmuDrF1Fm5c24eoG1gEoxv_81fDUMNHHUd2JGZmGyXoO_jShPd0Dk5q3SR3sb9T8Hy_3MxXqHh6WM9nBdIZYwOi0iprCVVSZxQbyZ2pMMNKqrpSkltrpck5ldKIigiDc2sUo9KNqqjlFZ2C691fE0NK0dVlH32r43dJcLkdpNwPMpJXO1Kb9gD9l7_eoVqD</recordid><startdate>20221102</startdate><enddate>20221102</enddate><creator>Ignat, Oana</creator><creator>Castro, Santiago</creator><creator>Zhou, Yuhang</creator><creator>Bao, Jiajun</creator><creator>Shan, Dandan</creator><creator>Mihalcea, Rada</creator><general>ACM</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20221102</creationdate><title>When Did It Happen? Duration-informed Temporal Localization of Narrated Actions in Vlogs</title><author>Ignat, Oana ; Castro, Santiago ; Zhou, Yuhang ; Bao, Jiajun ; Shan, Dandan ; Mihalcea, Rada</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a244t-38d9dd1398a230c85ecb040989fb985ddd8c75388c6b16c07dc9438e38eb3d5b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Information systems</topic><topic>Multimedia streaming</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ignat, Oana</creatorcontrib><creatorcontrib>Castro, Santiago</creatorcontrib><creatorcontrib>Zhou, Yuhang</creatorcontrib><creatorcontrib>Bao, Jiajun</creatorcontrib><creatorcontrib>Shan, Dandan</creatorcontrib><creatorcontrib>Mihalcea, Rada</creatorcontrib><collection>CrossRef</collection><jtitle>ACM transactions on multimedia computing communications and applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ignat, Oana</au><au>Castro, Santiago</au><au>Zhou, Yuhang</au><au>Bao, Jiajun</au><au>Shan, Dandan</au><au>Mihalcea, Rada</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>When Did It Happen? Duration-informed Temporal Localization of Narrated Actions in Vlogs</atitle><jtitle>ACM transactions on multimedia computing communications and applications</jtitle><stitle>ACM TOMM</stitle><date>2022-11-02</date><risdate>2022</risdate><volume>18</volume><issue>3s</issue><spage>1</spage><epage>18</epage><pages>1-18</pages><artnum>142</artnum><issn>1551-6857</issn><eissn>1551-6865</eissn><abstract>We consider the task of temporal human action localization in lifestyle vlogs. We introduce a novel dataset consisting of manual annotations of temporal localization for 13,000 narrated actions in 1,200 video clips. We present an extensive analysis of this data, which allows us to better understand how the language and visual modalities interact throughout the videos. We propose a simple yet effective method to localize the narrated actions based on their expected duration. Through several experiments and analyses, we show that our method brings complementary information with respect to previous methods, and leads to improvements over previous work for the task of temporal action localization.</abstract><cop>New York, NY</cop><pub>ACM</pub><doi>10.1145/3495211</doi><tpages>18</tpages></addata></record>
fulltext fulltext
identifier ISSN: 1551-6857
ispartof ACM transactions on multimedia computing communications and applications, 2022-11, Vol.18 (3s), p.1-18, Article 142
issn 1551-6857
1551-6865
language eng
recordid cdi_crossref_primary_10_1145_3495211
source ACM Digital Library Complete
subjects Information systems
Multimedia streaming
title When Did It Happen? Duration-informed Temporal Localization of Narrated Actions in Vlogs
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-12T13%3A40%3A33IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-acm_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=When%20Did%20It%20Happen?%20Duration-informed%20Temporal%20Localization%20of%20Narrated%20Actions%20in%20Vlogs&rft.jtitle=ACM%20transactions%20on%20multimedia%20computing%20communications%20and%20applications&rft.au=Ignat,%20Oana&rft.date=2022-11-02&rft.volume=18&rft.issue=3s&rft.spage=1&rft.epage=18&rft.pages=1-18&rft.artnum=142&rft.issn=1551-6857&rft.eissn=1551-6865&rft_id=info:doi/10.1145/3495211&rft_dat=%3Cacm_cross%3E3495211%3C/acm_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true