论文标题
Salypath360：全向图像的显着和扫描预测框架
SalyPath360: Saliency and Scanpath Prediction Framework for Omnidirectional Images
论文作者
论文摘要
本文介绍了一个新框架，以预测全向图像的视觉关注。我们体系结构的关键设置是显着图的同时预测给定刺激的相应扫描路径。该框架实现了由注意模块增强的完全编码器卷积神经网络，以产生代表性显着图。此外，还采用了辅助网络来通过软马克功能生成可能的视口固定点。后者允许从特征地图得出固定点。为了利用扫描扫描预测，通过利用基于编码器解码器的显着性图和基于扫描的显着热图来应用自适应的关节概率分布模型来构建最终的无偏见图。根据显着性和扫描路预测评估了所提出的框架，并将结果与Salient360上的最新方法进行了比较。数据集。结果显示了我们的框架的相关性以及此类架构对进一步全向视觉注意力预测任务的好处。
This paper introduces a new framework to predict visual attention of omnidirectional images. The key setup of our architecture is the simultaneous prediction of the saliency map and a corresponding scanpath for a given stimulus. The framework implements a fully encoder-decoder convolutional neural network augmented by an attention module to generate representative saliency maps. In addition, an auxiliary network is employed to generate probable viewport center fixation points through the SoftArgMax function. The latter allows to derive fixation points from feature maps. To take advantage of the scanpath prediction, an adaptive joint probability distribution model is then applied to construct the final unbiased saliency map by leveraging the encoder decoder-based saliency map and the scanpath-based saliency heatmap. The proposed framework was evaluated in terms of saliency and scanpath prediction, and the results were compared to state-of-the-art methods on Salient360! dataset. The results showed the relevance of our framework and the benefits of such architecture for further omnidirectional visual attention prediction tasks.
