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.

中文翻译：

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ABSNet：使用多任务卷积网络的基于美学的显着网络

作为分析视觉场景的智能视觉注意机制，视觉显着性已被证明与人脸等语义信息密切相关。尽管已经提出了许多语义信息引导的显着性模型，但据我们所知，情感域中的语义信息尚未用于显着性检测。审美，即综合了场景构图和对比度等因素的情感感知质量，当然可以使高度依赖于这些视觉因素的视觉注意力受益。在这封信中，我们提出了一种称为基于美学的显着性网络 (ABSNet) 的端到端多任务框架。我们使用三个常用的共享主干并为每个任务设计两个不同的分支。均方误差 (MSE) 损失和 Earth Mover' 联合采用距离（EMD）损失来交替训练共享网络和不同任务的单个分支，促进所提出的模型为视觉感知提取更有效的特征。此外，我们的模型是分辨率友好的，可以预测任意大小图像的显着性。已经表明，所提出的多任务方法优于单任务版本，并且优于最先进的显着性方法。