When we refer to an image that attracts our attention, it is natural to mention not only what is literally depicted in the image, but also the sentiments, thoughts and opinions that it invokes in ourselves. In this work we deviate from the standard mainstream tasks of associating tags or keywords to an image, or generating content image descriptions, and we introduce the novel task of automatically generate user comments for an image. We present a new dataset collected from the social media Pinterest and we propose a strategy based on building joint textual and visual user models, tailored to the specificity of the mentioned task. We conduct an extensive experimental analysis of our approach on both qualitative and quantitative terms, which allows to assess the value of the proposed approach and shows its encouraging results against several existing image-to-text methods.

What Would They Say? Predicting Users Comments in Pinterest / Gomez, Juan Carlos; Tommasi, Tatiana; Zoghbi, Susana; Moens, Marie Francine. - In: REVISTA IEEE AMÉRICA LATINA. - ISSN 1548-0992. - 14:4(2016), pp. 2013-2019. [10.1109/TLA.2016.7483548]

What Would They Say? Predicting Users Comments in Pinterest

TOMMASI, TATIANA
;
2016

Abstract

When we refer to an image that attracts our attention, it is natural to mention not only what is literally depicted in the image, but also the sentiments, thoughts and opinions that it invokes in ourselves. In this work we deviate from the standard mainstream tasks of associating tags or keywords to an image, or generating content image descriptions, and we introduce the novel task of automatically generate user comments for an image. We present a new dataset collected from the social media Pinterest and we propose a strategy based on building joint textual and visual user models, tailored to the specificity of the mentioned task. We conduct an extensive experimental analysis of our approach on both qualitative and quantitative terms, which allows to assess the value of the proposed approach and shows its encouraging results against several existing image-to-text methods.
2016
Deep-Learning Representation; Multimodal Clustering; Pinterest; Social Media; User Generated Content; Computer Science (all); Electrical and Electronic Engineering
01 Pubblicazione su rivista::01a Articolo in rivista
What Would They Say? Predicting Users Comments in Pinterest / Gomez, Juan Carlos; Tommasi, Tatiana; Zoghbi, Susana; Moens, Marie Francine. - In: REVISTA IEEE AMÉRICA LATINA. - ISSN 1548-0992. - 14:4(2016), pp. 2013-2019. [10.1109/TLA.2016.7483548]
File allegati a questo prodotto
File Dimensione Formato  
Gomez_Postprint_What-Would_2016.pdf

accesso aperto

Note: https://ieeexplore.ieee.org/document/7483548
Tipologia: Documento in Post-print (versione successiva alla peer review e accettata per la pubblicazione)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 653.08 kB
Formato Adobe PDF
653.08 kB Adobe PDF
Gomez_What-Would_2016.pdf

solo gestori archivio

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 591.48 kB
Formato Adobe PDF
591.48 kB Adobe PDF   Contatta l'autore

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/901367
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 5
  • ???jsp.display-item.citation.isi??? 3
social impact