  Deep Learning-based Unsupervised Salient Object Detection (USOD) mainly
relies on the noisy saliency pseudo labels that have been generated from
traditional handcraft methods or pre-trained networks. To cope with the noisy
labels problem, a class of methods focus on only easy samples with reliable
labels but ignore valuable knowledge in hard samples. In this paper, we propose
a novel USOD method to mine rich and accurate saliency knowledge from both easy
and hard samples. First, we propose a Confidence-aware Saliency Distilling
(CSD) strategy that scores samples conditioned on samples' confidences, which
guides the model to distill saliency knowledge from easy samples to hard
samples progressively. Second, we propose a Boundary-aware Texture Matching
(BTM) strategy to refine the boundaries of noisy labels by matching the
textures around the predicted boundary. Extensive experiments on RGB, RGB-D,
RGB-T, and video SOD benchmarks prove that our method achieves state-of-the-art
USOD performance.
