  Recent salient object detection (SOD) methods based on deep neural network
have achieved remarkable performance. However, most of existing SOD models
designed for low-resolution input perform poorly on high-resolution images due
to the contradiction between the sampling depth and the receptive field size.
Aiming at resolving this contradiction, we propose a novel one-stage framework
called Pyramid Grafting Network (PGNet), using transformer and CNN backbone to
extract features from different resolution images independently and then graft
the features from transformer branch to CNN branch. An attention-based
Cross-Model Grafting Module (CMGM) is proposed to enable CNN branch to combine
broken detailed information more holistically, guided by different source
feature during decoding process. Moreover, we design an Attention Guided Loss
(AGL) to explicitly supervise the attention matrix generated by CMGM to help
the network better interact with the attention from different models. We
contribute a new Ultra-High-Resolution Saliency Detection dataset UHRSD,
containing 5,920 images at 4K-8K resolutions. To our knowledge, it is the
largest dataset in both quantity and resolution for high-resolution SOD task,
which can be used for training and testing in future research. Sufficient
experiments on UHRSD and widely-used SOD datasets demonstrate that our method
achieves superior performance compared to the state-of-the-art methods.
