Visual Tracking by Adaptive Continual Meta-Learning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choi, Janghoon | - |
dc.contributor.author | Baik, Sungyong | - |
dc.contributor.author | Choi, Myungsub | - |
dc.contributor.author | Kwon, Junseok | - |
dc.contributor.author | Lee, Kyoung Mu | - |
dc.date.accessioned | 2023-08-01T07:01:42Z | - |
dc.date.available | 2023-08-01T07:01:42Z | - |
dc.date.created | 2023-07-21 | - |
dc.date.issued | 2022-01 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/188640 | - |
dc.description.abstract | We formulate the visual tracking problem as a semi-supervised continual learning problem, where only an initial frame is labeled. In contrast to conventional meta-learning based approaches that regard visual tracking as an instance detection problem with a focus on finding good weights for model initialization, we consider both initialization and online update processes simultaneously under our adaptive continual meta-learning framework. The proposed adaptive meta-learning strategy dynamically generates the hyperparameters needed for fast initialization and online update to achieve more robustness via adaptively regulating the learning process. In addition, our continual meta-learning approach based on knowledge distillation scheme helps the tracker adapt to new examples while retaining its knowledge on previously seen examples. We apply our proposed framework to deep learning-based tracking algorithm to obtain noticeable performance gains and competitive results against recent state-of-the-art tracking algorithms while performing at real-time speeds. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Visual Tracking by Adaptive Continual Meta-Learning | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Baik, Sungyong | - |
dc.identifier.doi | 10.1109/ACCESS.2022.3143809 | - |
dc.identifier.scopusid | 2-s2.0-85123299709 | - |
dc.identifier.wosid | 000747192300001 | - |
dc.identifier.bibliographicCitation | IEEE ACCESS, v.10, pp.9022 - 9035 | - |
dc.relation.isPartOf | IEEE ACCESS | - |
dc.citation.title | IEEE ACCESS | - |
dc.citation.volume | 10 | - |
dc.citation.startPage | 9022 | - |
dc.citation.endPage | 9035 | - |
dc.type.rims | ART | - |
dc.type.docType | 정기학술지(Article(Perspective Article포함)) | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | OBJECT TRACKING | - |
dc.subject.keywordAuthor | Visualization | - |
dc.subject.keywordAuthor | Target tracking | - |
dc.subject.keywordAuthor | Adaptation models | - |
dc.subject.keywordAuthor | Training | - |
dc.subject.keywordAuthor | Knowledge engineering | - |
dc.subject.keywordAuthor | Classification algorithms | - |
dc.subject.keywordAuthor | Task analysis | - |
dc.subject.keywordAuthor | Continual learning | - |
dc.subject.keywordAuthor | meta learning | - |
dc.subject.keywordAuthor | object tracking | - |
dc.subject.keywordAuthor | visual tracking | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/9682719 | - |
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