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dc.contributor.authorHou, Jingwenen_US
dc.contributor.authorYang, Shengen_US
dc.contributor.authorLin, Weisien_US
dc.identifier.citationHou, J., Yang, S., & Lin, W. (2020). Object-level attention for aesthetic rating distribution prediction. Proceedings of the 28th ACM International Conference on Multimedia, 816-824. doi:10.1145/3394171.3413695en_US
dc.description.abstractWe study the problem of image aesthetic assessment (IAA) and aim to automatically predict the image aesthetic quality in the form of discrete distribution, which is particularly important in IAA due to its nature of having possibly higher diversification of agreement for aesthetics. Previous works show the effectiveness of utilizing object-agnostic attention mechanisms to selectively concentrate on more contributive regions for IAA, e.g., attention is learned to weight pixels of input images when inferring aesthetic values. However, as suggested by some neuropsychology studies, the basic units of human attention are visual objects, i.e., the trace of human attention follows a series of objects. This inspires us to predict contributions of different regions at \textit{object level} for better aesthetics evaluation. With our framework, region-of-interests (RoIs) are proposed by an object detector, and each RoI is associated with a regional feature vector. Then the contribution of each regional feature to the aesthetics prediction is adaptively determined. To the best of our knowledge, this is the first work modeling object-level attention for IAA and experimental results confirm the superiority of our framework over previous relevant methods.en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.rights© 2020 Association for Computing Machinery (ACM). All rights reserved. This paper was published in 2020 ACM International Conference on Multimedia and is made available with permission of Association for Computing Machinery (ACM).en_US
dc.subjectEngineering::Computer science and engineering::Computing methodologies::Image processing and computer visionen_US
dc.titleObject-level attention for aesthetic rating distribution predictionen_US
dc.typeConference Paperen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.contributor.conference2020 ACM International Conference on Multimediaen_US
dc.description.versionAccepted versionen_US
dc.subject.keywordsImage Aesthetic Assessmenten_US
dc.subject.keywordsObject Detectionen_US
dc.citation.conferencelocationSeattle, WA, USAen_US
dc.description.acknowledgementTier-2 Fund MOE2016-T2-2-057(S)en_US
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