  Recently, unsupervised salient object detection (USOD) has gained increasing
attention due to its annotation-free nature. However, current methods mainly
focus on specific tasks such as RGB and RGB-D, neglecting the potential for
task migration. In this paper, we propose a unified USOD framework for generic
USOD tasks. Firstly, we propose a Progressive Curriculum Learning-based
Saliency Distilling (PCL-SD) mechanism to extract saliency cues from a
pre-trained deep network. This mechanism starts with easy samples and
progressively moves towards harder ones, to avoid initial interference caused
by hard samples. Afterwards, the obtained saliency cues are utilized to train a
saliency detector, and we employ a Self-rectify Pseudo-label Refinement (SPR)
mechanism to improve the quality of pseudo-labels. Finally, an adapter-tuning
method is devised to transfer the acquired saliency knowledge, leveraging
shared knowledge to attain superior transferring performance on the target
tasks. Extensive experiments on five representative SOD tasks confirm the
effectiveness and feasibility of our proposed method. Code and supplement
materials are available at https://github.com/I2-Multimedia-Lab/A2S-v3.
