@article {aAssens, title = {Scanpath and Saliency Prediction on 360 Degree Images}, journal = {Elsevier Signal Processing: Image Communication}, year = {2018}, abstract = {

We introduce deep neural networks for scanpath and saliency prediction trained on 360-degree images. The scanpath prediction model called SaltiNet is based on a temporal-aware novel representation of saliency information named the saliency volume. The first part of the network consists of a model trained to generate saliency volumes, whose parameters are fit by back-propagation using a binary cross entropy (BCE) loss over downsampled versions of the saliency volumes. Sampling strategies over these volumes are used to generate scanpaths over the 360-degree images. Our experiments show the advantages of using saliency volumes, and how they can be used for related tasks. We also show how a similar architecture achieves state-of-the-art performance for the related task of saliency map prediction. Our source code and trained models available here.

}, url = {https://www.sciencedirect.com/science/article/pii/S0923596518306209}, author = {Assens, Marc and McGuinness, Kevin and O{\textquoteright}Connor, N. and Xavier Gir{\'o}-i-Nieto} } @conference {cAssens, title = {SaltiNet: Scan-path Prediction on 360 Degree Images using Saliency Volumes}, booktitle = {ICCV Workshop on Egocentric Perception, Interaction and Computing}, year = {2017}, month = {07/2017}, publisher = {IEEE}, organization = {IEEE}, address = {Venice, Italy}, abstract = {

We introduce SaltiNet, a deep neural network for scanpath prediction trained on 360-degree images. The first part of the network consists of a model trained to generate saliency volumes, whose parameters are learned by back-propagation computed from a binary cross entropy (BCE) loss over downsampled versions of the saliency volumes. Sampling strategies over these volumes are used to generate scanpaths over the 360-degree images. Our experiments show the advantages of using saliency volumes, and how they can be used for related tasks.

Winner of three awards at the Salient 360 Challenge at IEEE ICME 2017 (Hong Kong): Best Scan Path, Best Student Scan-path and Audience Award.

SaltiNet: The Temporal Dimension of Visual Attention Models from Xavier Giro-i-Nieto
}, isbn = {978-1-5386-1034-3}, doi = {10.1109/ICCVW.2017.275}, url = {http://ieeexplore.ieee.org/document/8265485/}, author = {Assens, Marc and McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N.} }