Growing interests in RGB-D salient object detection (RGB-D SOD) have been
witnessed in recent years, owing partly to the popularity of depth sensors and
the rapid progress of deep learning techniques. Unfortunately, existing RGB-D
SOD methods typically demand large quantity of training images being thoroughly
annotated at pixel-level. The laborious and time-consuming manual annotation
has become a real bottleneck in various practical scenarios. On the other hand,
current unsupervised RGB-D SOD methods still heavily rely on handcrafted
feature representations. This inspires us to propose in this paper a deep
unsupervised RGB-D saliency detection approach, which requires no manual
pixel-level annotation during training. It is realized by two key ingredients
in our training pipeline. First, a depth-disentangled saliency update (DSU)
framework is designed to automatically produce pseudo-labels with iterative
follow-up refinements, which provides more trustworthy supervision signals for
training the saliency network. Second, an attentive training strategy is
introduced to tackle the issue of noisy pseudo-labels, by properly re-weighting
to highlight the more reliable pseudo-labels. Extensive experiments demonstrate
the superior efficiency and effectiveness of our approach in tackling the
challenging unsupervised RGB-D SOD scenarios. Moreover, our approach can also
be adapted to work in fully-supervised situation. Empirical studies show the
incorporation of our approach gives rise to notably performance improvement in
existing supervised RGB-D SOD models.

本研究提出了一种基于深度学习和无监督学习的 RGB-D 显著目标检测方法，利用基于深度学习的迭代自动伪标签生成和注意力训练策略，无需人工像素级注释即可在无监督场景下提高 RGB-D 显著目标检测的效率和效果，同时也适用于全监督情况下的场景。