Towards End-to-End Unsupervised Saliency Detection with Self-Supervised
  Top-Down Context
Yicheng Song Shuyong Gao Haozhe Xing Yiting Cheng Yan Wang Wenqiang Zhang
Unsupervised salient object detection aims to detect salient objects without
using supervision signals eliminating the tedious task of manually labeling
salient objects. To improve training efficiency, end-to-end methods for USOD
have been proposed as a promising alternative. However, current solutions rely
heavily on noisy handcraft labels and fail to mine rich semantic information
from deep features. In this paper, we propose a self-supervised end-to-end
salient object detection framework via top-down context. Specifically,
motivated by contrastive learning, we exploit the self-localization from the
deepest feature to construct the location maps which are then leveraged to
learn the most instructive segmentation guidance. Further considering the lack
of detailed information in deepest features, we exploit the detail-boosting
refiner module to enrich the location labels with details. Moreover, we observe
that due to lack of supervision, current unsupervised saliency models tend to
detect non-salient objects that are salient in some other samples of
corresponding scenarios. To address this widespread issue, we design a novel
Unsupervised Non-Salient Suppression (UNSS) method developing the ability to
ignore non-salient objects. Extensive experiments on benchmark datasets
demonstrate that our method achieves leading performance among the recent
end-to-end methods and most of the multi-stage solutions. The code is
available.