  Saliency map estimation in computer vision aims to estimate the locations
where people gaze in images. Since people tend to look at objects in images,
the parameters of the model pretrained on ImageNet for image classification are
useful for the saliency map estimation. However, there is no research on the
relationship between the image classification accuracy and the performance of
the saliency map estimation. In this paper, it is shown that there is a strong
correlation between image classification accuracy and saliency map estimation
accuracy. We also investigated the effective architecture based on multi scale
images and the upsampling layers to refine the saliency-map resolution. Our
model achieved the state-of-the-art accuracy on the PASCAL-S, OSIE, and MIT1003
datasets. In the MIT Saliency Benchmark, our model achieved the best
performance in some metrics and competitive results in the other metrics.
