Please use this identifier to cite or link to this item:
https://open.uns.ac.rs/handle/123456789/279
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Siarohin A. | en_US |
dc.contributor.author | Zen G. | en_US |
dc.contributor.author | Majtanović, Cveta | en_US |
dc.contributor.author | Alameda-Pineda X. | en_US |
dc.contributor.author | Ricci E. | en_US |
dc.contributor.author | Sebe N. | en_US |
dc.date.accessioned | 2019-09-23T10:05:43Z | - |
dc.date.available | 2019-09-23T10:05:43Z | - |
dc.date.issued | 2019-06-01 | - |
dc.identifier.issn | 15516857 | en_US |
dc.identifier.uri | https://open.uns.ac.rs/handle/123456789/279 | - |
dc.description.abstract | © 2019 Association for Computing Machinery. Recent works in computer vision and multimedia have shown that image memorability can be automatically inferred exploiting powerful deep-learning models. This article advances the state of the art in this area by addressing a novel and more challenging issue: "Given an arbitrary input image, can we make it more memorable? " To tackle this problem, we introduce an approach based on an editing-by-applying-filters paradigm: Given an input image, we propose to automatically retrieve a set of "style seeds," i.e., a set of style images that, applied to the input image through a neural style transfer algorithm, provide the highest increase in memorability. We show the effectiveness of the proposed approach with experiments on the publicly available LaMem dataset, performing both a quantitative evaluation and a user study. To demonstrate the flexibility of the proposed framework, we also analyze the impact of different implementation choices, such as using different state-of-the-art neural style transfer methods. Finally, we show several qualitative results to provide additional insights on the link between image style and memorability. | en |
dc.relation.ispartof | ACM Transactions on Multimedia Computing, Communications and Applications | en |
dc.title | Increasing image memorability with neural style transfer | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.doi | 10.1145/3311781 | - |
dc.identifier.scopus | 2-s2.0-85067225680 | - |
dc.identifier.url | https://api.elsevier.com/content/abstract/scopus_id/85067225680 | - |
dc.description.version | Unknown | en_US |
dc.relation.issue | 2 | en |
dc.relation.volume | 15 | en |
item.fulltext | No Fulltext | - |
item.grantfulltext | none | - |
Appears in Collections: | Naučne i umetničke publikacije |
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