使用可学习赤道偏置的全向显著性图多尺度估计
Multi-Scale Estimation for Omni-Directional Saliency Maps Using Learnable Equator Bias
AI总结：
本文提出一种全向图像显著性图估计模型，通过提取多视角2D图像并引入可学习的赤道偏置层替代中心偏置层进行微调，结合多尺度整合机制提升了检测精度。
摘要：
全向图像已被广泛应用于各种领域。对于这些应用而言，估计使用头戴式显示器时的注视点概率分布显著性图，以检测全向图像中的重要区域是非常有用的。本文提出了一种新颖的全向图像显著性图估计模型，通过从不同方向和视角的全向图像中提取重叠的二维（2D）平面图像来实现。虽然2D显著性图在图像中心往往具有高概率（中心偏置），但当使用头戴式显示器时，全向显著性图的高概率区域出现在水平方向（赤道偏置）。因此，通过将中心偏置层替换为以提取2D平面图像的仰角为条件的赤道偏置层，利用全向数据集对带有中心偏置层的2D显著性模型进行了微调。显著性数据集中全向图像的有限可用性，可以通过使用由大量带有2D显著性图真值的训练图像预训练的成熟2D显著性模型来弥补。此外，本文还提出了一种多尺度估计方法，通过提取多个视角的2D图像，利用可变的感受野来检测不同大小的物体。从多个视角估计的显著性图通过在整合层中计算的像素级注意力权重进行整合，以为每个物体加权最佳尺度。所提出的方法使用公开可用的数据集和全向显著性图评估指标进行了评估。结果证实，所提出的方法提高了显著性图的准确性。
英文摘要：
Omni-directional images have been used in wide range of applications. For the applications, it would be useful to estimate saliency maps representing probability distributions of gazing points with a head-mounted display, to detect important regions in the omni-directional images. This paper proposes a novel saliency-map estimation model for the omni-directional images by extracting overlapping 2-dimensional (2D) plane images from omni-directional images at various directions and angles of view. While 2D saliency maps tend to have high probability at the center of images (center bias), the high-probability region appears at horizontal directions in omni-directional saliency maps when a head-mounted display is used (equator bias). Therefore, the 2D saliency model with a center-bias layer was fine-tuned with an omni-directional dataset by replacing the center-bias layer to an equator-bias layer conditioned on the elevation angle for the extraction of the 2D plane image. The limited availability of omni-directional images in saliency datasets can be compensated by using the well-established 2D saliency model pretrained by a large number of training images with the ground truth of 2D saliency maps. In addition, this paper proposes a multi-scale estimation method by extracting 2D images in multiple angles of view to detect objects of various sizes with variable receptive fields. The saliency maps estimated from the multiple angles of view were integrated by using pixel-wise attention weights calculated in an integration layer for weighting the optimal scale to each object. The proposed method was evaluated using a publicly available dataset with evaluation metrics for omni-directional saliency maps. It was confirmed that the accuracy of the saliency maps was improved by the proposed method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
