%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{Benkhedda_ACMMM_2017,
         author = {Benkhedda, Yassir and Santani, Darshan and Gatica-Perez, Daniel},
       projects = {Idiap},
          title = {Venues in Social Media: Examining Ambiance Perception Through Scene Semantics},
      booktitle = {Proceedings of the 25th ACM International Conference on Multimedia, ACM, 2017},
           year = {2017},
       abstract = {We address the question of what visual cues, including scene objects
and demographic attributes, contribute to the automatic inference
of perceived ambiance in social media venues. We first use a stateof-
art, deep scene semantic parsing method and a face attribute
extractor to understand how different cues present in a scene relate
to human perception of ambiance on Foursquare images of social
venues.We then analyze correlational links between visual cues and
thirteen ambiance variables, as well as the ability of the semantic
attributes to automatically infer place ambiance. We study the
effect of the type and amount of image data used for learning, and
compare regression results to previous work, showing that the
proposed approach results in marginal-to-moderate performance
increase for up to ten of the ambiance dimensions, depending on
the corpus.},
            pdf = {https://publications.idiap.ch/attachments/papers/2017/Benkhedda_ACMMM_2017.pdf}
}