  Weakly-supervised salient object detection (WSOD) aims to develop saliency
models using image-level annotations. Despite of the success of previous works,
explorations on an effective training strategy for the saliency network and
accurate matches between image-level annotations and salient objects are still
inadequate. In this work, 1) we propose a self-calibrated training strategy by
explicitly establishing a mutual calibration loop between pseudo labels and
network predictions, liberating the saliency network from error-prone
propagation caused by pseudo labels. 2) we prove that even a much smaller
dataset (merely 1.8% of ImageNet) with well-matched annotations can facilitate
models to achieve better performance as well as generalizability. This sheds
new light on the development of WSOD and encourages more contributions to the
community. Comprehensive experiments demonstrate that our method outperforms
all the existing WSOD methods by adopting the self-calibrated strategy only.
Steady improvements are further achieved by training on the proposed dataset.
Additionally, our method achieves 94.7% of the performance of fully-supervised
methods on average. And what is more, the fully supervised models adopting our
predicted results as "ground truths" achieve successful results (95.6% for
BASNet and 97.3% for ITSD on F-measure), while costing only 0.32% of labeling
time for pixel-level annotation.
