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Viraliency: Pooling Local Virality

Xavier Alameda-Pineda 1, 2 Andrea Pilzer 2 Dan Xu 2 Nicu Sebe 2 Elisa Ricci 3, 4 
1 PERCEPTION - Interpretation and Modelling of Images and Videos
Inria Grenoble - Rhône-Alpes, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology, LJK - Laboratoire Jean Kuntzmann
Abstract : In our overly-connected world, the automatic recognition of virality – the quality of an image or video to be rapidly and widely spread in social networks – is of crucial importance, and has recently awaken the interest of the computer vision community. Concurrently, recent progress in deep learning architectures showed that global pooling strategies allow the extraction of activation maps, which highlight the parts of the image most likely to contain instances of a certain class. We extend this concept by introducing a pooling layer that learns the size of the support area to be averaged: the learned top-N average (LENA) pooling. We hypothesize that the latent concepts (feature maps) describing virality may require such a rich pooling strategy. We assess the effectiveness of the LENA layer by appending it on top of a convolutional siamese architecture and evaluate its performance on the task of predicting and localizing virality. We report experiments on two publicly available datasets annotated for virality and show that our method outperforms state-of-the-art approaches.
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Submitted on : Friday, July 7, 2017 - 10:49:34 AM
Last modification on : Tuesday, November 29, 2022 - 4:00:55 AM
Long-term archiving on: : Wednesday, January 24, 2018 - 8:06:18 AM


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Xavier Alameda-Pineda, Andrea Pilzer, Dan Xu, Nicu Sebe, Elisa Ricci. Viraliency: Pooling Local Virality. IEEE Conference on Computer Vision and Pattern Recognition, Jul 2017, Honolulu, Hawaii, United States. pp.484-492, ⟨10.1109/CVPR.2017.59⟩. ⟨hal-01558137⟩



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