论文标题
基于固定的360°基准数据集用于显着对象检测
A Fixation-based 360° Benchmark Dataset for Salient Object Detection
论文作者
论文摘要
全景内容中的固定预测（FP）以及虚拟现实（VR）应用的蓬勃发展趋势得到了广泛研究。然而，由于缺乏代表具有像素级注释的真实场景的数据集，很少在360°（或全向图像）中探索视觉显着性检测（SOD）的另一个问题。为此，我们收集具有具有挑战性的场景和多个对象类的107个等级全景。基于FP和显性显着性判断之间的一致性，我们进一步在收集的图像上手动注释了1,165个显着物体，并在真实人类眼睛固定图的指导下用精确的面具进行了精确的掩模。然后，通过应用多个基于基于立方体投射的微调方法，将六个最先进的SOD模型在建议的基于固定的360°图像数据集（F-360ISOD）上进行基准测试。实验结果表明，当全景图像中用于SOD时，当前方法的局限性，这表明所提出的数据集具有挑战性。还讨论了360°SOD的关键问题。所提出的数据集可在https://github.com/panoash/f-360isod上找到。
Fixation prediction (FP) in panoramic contents has been widely investigated along with the booming trend of virtual reality (VR) applications. However, another issue within the field of visual saliency, salient object detection (SOD), has been seldom explored in 360° (or omnidirectional) images due to the lack of datasets representative of real scenes with pixel-level annotations. Toward this end, we collect 107 equirectangular panoramas with challenging scenes and multiple object classes. Based on the consistency between FP and explicit saliency judgements, we further manually annotate 1,165 salient objects over the collected images with precise masks under the guidance of real human eye fixation maps. Six state-of-the-art SOD models are then benchmarked on the proposed fixation-based 360° image dataset (F-360iSOD), by applying a multiple cubic projection-based fine-tuning method. Experimental results show a limitation of the current methods when used for SOD in panoramic images, which indicates the proposed dataset is challenging. Key issues for 360° SOD is also discussed. The proposed dataset is available at https://github.com/PanoAsh/F-360iSOD.
