  We present an advanced study on more challenging high-resolution salient
object detection (HRSOD) from both dataset and network framework perspectives.
To compensate for the lack of HRSOD dataset, we thoughtfully collect a
large-scale high resolution salient object detection dataset, called UHRSD,
containing 5,920 images from real-world complex scenarios at 4K-8K resolutions.
All the images are finely annotated in pixel-level, far exceeding previous
low-resolution SOD datasets. Aiming at overcoming the contradiction between the
sampling depth and the receptive field size in the past methods, we propose a
novel one-stage framework for HR-SOD task using pyramid grafting mechanism. In
general, transformer-based and CNN-based backbones are adopted to extract
features from different resolution images independently and then these features
are grafted 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 branches.
Comprehensive experiments on UHRSD and widely-used SOD datasets demonstrate
that our method can simultaneously locate salient object and preserve rich
details, outperforming state-of-the-art methods. To verify the generalization
ability of the proposed framework, we apply it to the camouflaged object
detection (COD) task. Notably, our method performs superior to most
state-of-the-art COD methods without bells and whistles.
