  We present a conceptually simple self-supervised method for saliency
detection. Our method generates and uses pseudo-ground truth labels for
training. The generated pseudo-GT labels don't require any kind of human
annotations (e.g., pixel-wise labels or weak labels like scribbles). Recent
works show that features extracted from classification tasks provide important
saliency cues like structure and semantic information of salient objects in the
image. Our method, called 3SD, exploits this idea by adding a branch for a
self-supervised classification task in parallel with salient object detection,
to obtain class activation maps (CAM maps). These CAM maps along with the edges
of the input image are used to generate the pseudo-GT saliency maps to train
our 3SD network. Specifically, we propose a contrastive learning-based training
on multiple image patches for the classification task. We show the multi-patch
classification with contrastive loss improves the quality of the CAM maps
compared to naive classification on the entire image. Experiments on six
benchmark datasets demonstrate that without any labels, our 3SD method
outperforms all existing weakly supervised and unsupervised methods, and its
performance is on par with the fully-supervised methods. Code is available at
:https://github.com/rajeevyasarla/3SD
