WEAKLY-SUPERVISED MOMENT RETRIEVAL NETWORK FOR VIDEO CORPUS MOMENT RETRIEVAL
DC Field | Value | Language |
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dc.contributor.author | Yoon, Sunjae | - |
dc.contributor.author | Kim, Dahyun | - |
dc.contributor.author | Hong, Ji Woo | - |
dc.contributor.author | Kim, Junyeong | - |
dc.contributor.author | Kim, Kookhoi | - |
dc.contributor.author | Yoo, Chang D. | - |
dc.date.accessioned | 2023-03-08T10:30:03Z | - |
dc.date.available | 2023-03-08T10:30:03Z | - |
dc.date.issued | 2021-09 | - |
dc.identifier.issn | 1522-4880 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/62184 | - |
dc.description.abstract | This paper proposes Weakly-supervised Moment Retrieval Network (WMRN) for Video Corpus Moment Retrieval (VCMR), which retrieves pertinent temporal moments related to natural language query in a large video corpus. Previous methods for VCMR require full supervision of temporal boundary information for training, which involves a labor-intensive process of annotating the boundaries in a large number of videos. To leverage this, the proposed WMRN performs VCMR in a weakly-supervised manner, where WMRN is learned without ground-truth labels but only with video and text queries. For weakly-supervised VCMR, WMRN addresses the following two limitations of prior methods: (1) Blurry attention over video features due to redundant video candidate proposals generation, (2) Insufficient learning due to weak supervision only with video-query pairs. To this end, WMRN is based on (1) Text Guided Proposal Generation (TGPG) that effectively generates text guided multi-scale video proposals in the prospective region related to query, and (2) Hard Negative Proposal Sampling (HNPS) that enhances video-language alignment via extracting negative video proposals in positive video sample for contrastive learning. Experimental results show that WMRN achieves state-of-the-art performance on TVR and DiDeMo benchmarks in the weakly-supervised setting. To validate the attainments of proposed components of WMRN, comprehensive ablation studies and qualitative analysis are conducted. | - |
dc.format.extent | 5 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | IEEE | - |
dc.title | WEAKLY-SUPERVISED MOMENT RETRIEVAL NETWORK FOR VIDEO CORPUS MOMENT RETRIEVAL | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/ICIP42928.2021.9506218 | - |
dc.identifier.bibliographicCitation | 2021 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), pp 534 - 538 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000819455100108 | - |
dc.identifier.scopusid | 2-s2.0-85125582233 | - |
dc.citation.endPage | 538 | - |
dc.citation.startPage | 534 | - |
dc.citation.title | 2021 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | - |
dc.type.docType | Proceedings Paper | - |
dc.publisher.location | 미국 | - |
dc.subject.keywordAuthor | Multi-modal video corpus moment retrieval | - |
dc.subject.keywordAuthor | Weakly-supervised learning | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Imaging Science & Photographic Technology | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Software Engineering | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Imaging Science & Photographic Technology | - |
dc.description.journalRegisteredClass | scopus | - |
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