**Abstract**

Most of the saliency methods are evaluated for their ability to generate saliency maps, and not for their functionality in a complete vision pipeline, for instance, image classification or salient object subitizing. In this work, we introduce saliency subitizing as the weak supervision. This task is inspired by the ability of people to quickly and accurately identify the number of items within the subitizing range (e.g., 1 to 4 different types of things). This means that the subitizing information will tell us the number of featured objects in a given image. To this end, we propose a saliency subitizing process (SSP) as a first approximation to learn saliency detection, without the need for any unsupervised methods or some random seeds. We conduct extensive experiments on two benchmark datasets (Toronto and SID4VAM). The experimental results show that our method outperforms other weakly supervised methods and even performs comparable to some fully supervised methods as a first approximation.

**Keywords:** saliency prediction, subitizing, object recognition, deep learning and convolutional neural network
