Light field saliency detection can leverage the rich visual features of light field(LF) to highlight the salient regions, but existing CNN-based saliency detection methods are specifically designed for RGB image, not for light field. To tackle this problem, a three-stream cross-modal feature aggregation network is proposed for 4D light field saliency detection. To fully utilize the rich visual features of light field, three sub-networks are set up to analyse focal stack, all-focus image, and depth map respectively. Then, feature aggregation modules are used to aggregate cross-level features in a top-down manner. Finally, a cross-modal feature fusion module is designed to fuse the aggregated features of various modalities from the three sub-networks, which can identify salient object quickly and precisely. Extensive experiments on three benchmark datasets show that the effectiveness and superiority of the proposed algorithm qualitatively and quantitatively on five evaluation metrics compared with state-of-the-art(SOTA) methods.

中文翻译：

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用于光场显着目标检测的三流跨模态特征聚合网络

光场显着性检测可以利用光场（LF）丰富的视觉特征来突出显着区域，但现有的基于 CNN 的显着性检测方法是专门针对 RGB 图像设计的，而不是针对光场设计的。为了解决这个问题，提出了一种用于 4D 光场显着性检测的三流跨模态特征聚合网络。为了充分利用光场丰富的视觉特征，设置了三个子网络，分别对焦栈、全焦图像和深度图进行分析。然后，使用特征聚合模块以自上而下的方式聚合跨级特征。最后，设计了一个跨模态特征融合模块来融合来自三个子网络的各种模态的聚合特征，可以快速准确地识别显着对象。