# Salient Object Detection with Semantic Priors Source: [Salient Object Detection with Semantic Priors]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:38:15.653Z Authors: Tam V. Nguyen, Luoqi Liu Published: 2017-05-23T12:24:09.000Z ## Abstract Salient object detection has increasingly become a popular topic in cognitive and computational sciences, including computer vision and artificial intelligence research. In this paper, we propose integrating \\textit{semantic priors} into the salient object detection process. Our algorithm consists of three basic steps. Firstly, the explicit saliency map is obtained based on the semantic segmentation refined by the explicit saliency priors learned from the data. Next, the implicit saliency map is computed based on a trained model which maps the implicit saliency priors embedded into regional features with the saliency values. Finally, the explicit semantic map and the implicit map are adaptively fused to form a pixel-accurate saliency map which uniformly covers the objects of interest. We further evaluate the proposed framework on two challenging datasets, namely, ECSSD and HKUIS. The extensive experimental results demonstrate that our method outperforms other state-of-the-art methods. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()