Gated bidirectional feature pyramid network for accurate one-shot detection

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dc.contributor.authorWoo, Sanghyunko
dc.contributor.authorHwang, Soonminko
dc.contributor.authorJang, Ho-Deokko
dc.contributor.authorKweon, In Soko
dc.date.accessioned2019-06-19T01:10:33Z-
dc.date.available2019-06-19T01:10:33Z-
dc.date.created2019-06-18-
dc.date.created2019-06-18-
dc.date.issued2019-06-
dc.identifier.citationMACHINE VISION AND APPLICATIONS, v.30, no.4, pp.543 - 555-
dc.identifier.issn0932-8092-
dc.identifier.urihttp://hdl.handle.net/10203/262733-
dc.description.abstractDespite recent advances in machine learning, it is still challenging to realize real-time and accurate detection in images. The recently proposed StairNet detector (Sanghyun et al. in Proceedings of winter conference on applications of computer vision (WACV), 2018), one of the strongest one-stage detectors, tackles this issue by using a SSD in conjunction with a top-down enrichment module. However, the StairNet approach misses the finer localization information which can be obtained from the lower layer and lacks a feature selection mechanism, which can lead to suboptimal features during the merging step. In this paper, we propose what is termed the gated bidirectional feature pyramid network (GBFPN), a simple and effective architecture that provides a significant improvement over the baseline model, StairNet. The overall network is composed of three parts: a bottom-up pathway, a top-down pathway, and a gating module. Given the multi-scale feature pyramid of deep convolutional network, two separate pathways introduce both finer localization cues and high-level semantics. In each pathway, the gating module dynamically re-weights the features before the combining step, transmitting only the informative features. Placing GBFPN on top of a basic one-stage detector SSD, our method shows state-of-the-art results.-
dc.languageEnglish-
dc.publisherSPRINGER-
dc.titleGated bidirectional feature pyramid network for accurate one-shot detection-
dc.typeArticle-
dc.identifier.wosid000469483000001-
dc.identifier.scopusid2-s2.0-85062968685-
dc.type.rimsART-
dc.citation.volume30-
dc.citation.issue4-
dc.citation.beginningpage543-
dc.citation.endingpage555-
dc.citation.publicationnameMACHINE VISION AND APPLICATIONS-
dc.identifier.doi10.1007/s00138-019-01017-9-
dc.contributor.localauthorKweon, In So-
dc.contributor.nonIdAuthorJang, Ho-Deok-
dc.description.isOpenAccessY-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorObject detection-
dc.subject.keywordAuthorMulti-scale aggregation-
dc.subject.keywordAuthorOne-shot detection-
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