  Visual salient object detection (SOD) aims at finding the salient object(s)
that attract human attention, while camouflaged object detection (COD) on the
contrary intends to discover the camouflaged object(s) that hidden in the
surrounding. In this paper, we propose a paradigm of leveraging the
contradictory information to enhance the detection ability of both salient
object detection and camouflaged object detection. We start by exploiting the
easy positive samples in the COD dataset to serve as hard positive samples in
the SOD task to improve the robustness of the SOD model. Then, we introduce a
similarity measure module to explicitly model the contradicting attributes of
these two tasks. Furthermore, considering the uncertainty of labeling in both
tasks' datasets, we propose an adversarial learning network to achieve both
higher order similarity measure and network confidence estimation. Experimental
results on benchmark datasets demonstrate that our solution leads to
state-of-the-art (SOTA) performance for both tasks.
