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

Pyramid Grafting Network for One-Stage High Resolution Saliency Detection