In this work, we propose to utilize Convolutional Neural Networks to boost
the performance of depth-induced salient object detection by capturing the
high-level representative features for depth modality. We formulate the
depth-induced saliency detection as a CNN-based cross-modal transfer problem to
bridge the gap between the "data-hungry" nature of CNNs and the unavailability
of sufficient labeled training data in depth modality. In the proposed
approach, we leverage the auxiliary data from the source modality effectively
by training the RGB saliency detection network to obtain the task-specific
pre-understanding layers for the target modality. Meanwhile, we exploit the
depth-specific information by pre-training a modality classification network
that encourages modal-specific representations during the optimizing course.
Thus, it could make the feature representations of the RGB and depth modalities
as discriminative as possible. These two modules are pre-trained independently
and then stitched to initialize and optimize the eventual depth-induced
saliency detection model. Experiments demonstrate the effectiveness of the
proposed novel pre-training strategy as well as the significant and consistent
improvements of the proposed approach over other state-of-the-art methods.

本文研究使用卷积神经网络，通过跨模态传输问题对深度引导显著性检测进行提高。在这个方法中，我们有效地利用源模态的辅助数据来训练红绿蓝（RGB）显著性检测网络，并利用深度特定信息来预训练模态分类网络。这两个模块都是独立训练的，然后被拼接起来来初始化并优化最终的深度引导显著性检测模型。实验证明，这种预训练策略及所提出的方法都可以显著且一致地提高性能。