  Deep neural network based methods have made a significant breakthrough in
salient object detection. However, they are typically limited to input images
with low resolutions ($400\times400$ pixels or less). Little effort has been
made to train deep neural networks to directly handle salient object detection
in very high-resolution images. This paper pushes forward high-resolution
saliency detection, and contributes a new dataset, named High-Resolution
Salient Object Detection (HRSOD). To our best knowledge, HRSOD is the first
high-resolution saliency detection dataset to date. As another contribution, we
also propose a novel approach, which incorporates both global semantic
information and local high-resolution details, to address this challenging
task. More specifically, our approach consists of a Global Semantic Network
(GSN), a Local Refinement Network (LRN) and a Global-Local Fusion Network
(GLFN). GSN extracts the global semantic information based on down-sampled
entire image. Guided by the results of GSN, LRN focuses on some local regions
and progressively produces high-resolution predictions. GLFN is further
proposed to enforce spatial consistency and boost performance. Experiments
illustrate that our method outperforms existing state-of-the-art methods on
high-resolution saliency datasets by a large margin, and achieves comparable or
even better performance than them on widely-used saliency benchmarks. The HRSOD
dataset is available at https://github.com/yi94code/HRSOD.
