  Salient Object Detection (SOD) aims to identify and segment the most
conspicuous objects in an image or video. As an important pre-processing step,
it has many potential applications in multimedia and vision tasks. With the
advance of imaging devices, SOD with high-resolution images is of great demand,
recently. However, traditional SOD methods are largely limited to
low-resolution images, making them difficult to adapt to the development of
High-Resolution SOD (HRSOD). Although some HRSOD methods emerge, there are no
large enough datasets for training and evaluating. Besides, current HRSOD
methods generally produce incomplete object regions and irregular object
boundaries. To address above issues, in this work, we first propose a new
HRS10K dataset, which contains 10,500 high-quality annotated images at 2K-8K
resolution. As far as we know, it is the largest dataset for the HRSOD task,
which will significantly help future works in training and evaluating models.
Furthermore, to improve the HRSOD performance, we propose a novel Recurrent
Multi-scale Transformer (RMFormer), which recurrently utilizes shared
Transformers and multi-scale refinement architectures. Thus, high-resolution
saliency maps can be generated with the guidance of lower-resolution
predictions. Extensive experiments on both high-resolution and low-resolution
benchmarks show the effectiveness and superiority of the proposed framework.
The source code and dataset are released at:
https://github.com/DrowsyMon/RMFormer.
