The 2D image based salient object detection (SOD) has been extensively explored, while the 360° omnidirectional image based SOD has received less research attention and there exist three major bottlenecks that are limiting its performance. Firstly, the currently available training data is insufficient for the training of 360° SOD deep model. Secondly, the visual distortions in 360° omnidirectional images usually result in large feature gap between 360° images and 2D images; consequently, the widely used stage-wise training—a widely-used solution to alleviate the training data shortage problem, becomes infeasible when conducing SOD in 360° omnidirectional images. Thirdly, the existing 360° SOD approach has followed a multi-task methodology that performs salient object localization and segmentation-like saliency refinement at the same time, being faced with extremely large problem domain, making the training data shortage dilemma even worse. To tackle all these issues, this paper divides the 360° SOD into a multi-staqe task, the key rationale of which is to decompose the original complex problem domain into sequential easy sub problems that only demand for small-scale training data. Meanwhile, we learn how to rank the “object-level semantical saliency”, aiming to locate salient viewpoints and objects accurately. Specifically, to alleviate the training data shortage problem, we have released a novel dataset named 360-SSOD, containing 1,105 360° omnidirectional images with manually annotated object-level saliency ground truth, whose semantical distribution is more balanced than that of the existing dataset. Also, we have compared the proposed method with 13 SOTA methods, and all quantitative results have demonstrated the performance superiority.

中文翻译：

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通过对象级语义显着性分级在360°全向图像中进行逐级显着对象检测。

基于2D图像的显着目标检测（SOD）已被广泛探索，而基于360°全向图像的SOD受到的研究较少，并且存在三个主要瓶颈限制了其性能。首先，当前可用的训练数据不足以训练360°SOD深层模型。其次，360°全向图像的视觉失真通常会导致360°图像和2D图像之间出现较大的特征间隙；因此，当在360°全向图像中生成SOD时，广泛使用的分阶段训练（一种用于减轻训练数据短缺问题的广泛解决方案）变得不可行。第三，现有的360°SOD方法采用了多任务方法，该方法可以同时执行显着的对象定位和类似分段的显着性细化，面临极大的问题领域，使培训数据短缺的困境更加严重。为了解决所有这些问题，本文将360°SOD划分为多步任务，其主要原理是将原始复杂问题域分解为仅需要小规模训练数据的顺序简单子问题。同时，我们学习如何对“对象级别的语义显着性”进行排名，以准确定位重要的观点和对象。具体来说，为缓解训练数据短缺的问题，我们发布了一个名为360-SSOD的新数据集，其中包含1,105个360°全向图像，这些图像带有手动注释的对象级显着性基本事实，其语义分布比现有数据集更加均衡。此外，我们将建议的方法与13种SOTA方法进行了比较，