  In this technical report, we present our publicly downloadable implementation
of the SALICON saliency model. At the time of this writing, SALICON is one of
the top performing saliency models on the MIT 300 fixation prediction dataset
which evaluates how well an algorithm is able to predict where humans would
look in a given image. Recently, numerous models have achieved state-of-the-art
performance on this benchmark, but none of the top 5 performing models
(including SALICON) are available for download. To address this issue, we have
created a publicly downloadable implementation of the SALICON model. It is our
hope that our model will engender further research in visual attention modeling
by providing a baseline for comparison of other algorithms and a platform for
extending this implementation. The model we provide supports both training and
testing, enabling researchers to quickly fine-tune the model on their own
dataset. We also provide a pre-trained model and code for those users who only
need to generate saliency maps for images without training their own model.
