ABSNet: Aesthetics-Based Saliency Network Using Multi-Task Convolutional Network

As a smart visual attention mechanism to analyze visual scenes, visual saliency has been shown to closely correlate with semantic information such as faces. Although many semantic-information-guided saliency models have been proposed, to the best of our knowledge, no semantic information in affective domain has been employed for saliency detection. Aesthetic, the affective perceptual quality that integrates factors like scene composition and contrast, can certainly benefit visual attention that highly depends on these visual factors. In this letter, we propose an end-to-end multi-task framework called aesthetics-based saliency network (ABSNet). We use three commonly-used shared backbones and design two distinct branches for each task. Mean square error (MSE) loss and Earth Mover's Distance (EMD) loss are jointly adopted to alternately train the shared network and individual branch for different tasks, facilitating the proposed model to extract more effective features for visual perception. Moreover, our model is resolution-friendly to predict saliency for images of arbitrary size. It has been shown that the proposed multi-task method is superior over single-task version and outperforms state-of-the-art saliency methods.