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.

翻译：显著目标检测旨在识别并分割图像或视频中最显著的对象。作为重要的预处理步骤，其在多媒体和视觉任务中具有诸多潜在应用。随着成像设备的发展，高分辨率图像的显著目标检测需求日益增长。然而，传统显著目标检测方法主要局限于低分辨率图像，难以适应高分辨率显著目标检测的发展趋势。尽管已有部分高分辨率显著目标检测方法涌现，但目前尚缺乏足够规模的数据集用于训练与评估。此外，现有高分辨率显著目标检测方法通常产生不完整的目标区域和不规则的目标边界。针对上述问题，本文首先提出全新的HRS10K数据集，包含10500张2K-8K分辨率的高质量标注图像。据我们所知，这是目前规模最大的高分辨率显著目标检测任务数据集，将有效助力未来模型训练与评估工作。为进一步提升检测性能，我们提出递归多尺度Transformer（RMFormer），该模型通过共享Transformer与多尺度细化架构的递归机制，在低分辨率预测引导下生成高分辨率显著性图。在高低分辨率基准上的大量实验表明，所提框架具有有效性与优越性。源代码与数据集已开源至：https://github.com/DrowsyMon/RMFormer。