Uncertainty-aware Joint Salient Object and Camouflaged Object Detection

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(1).

Automated summary

This paper proposes a method to enhance the detection ability of visual salient object detection and camouflaged object detection by leveraging contradictory information. By using easy positive samples in the COD dataset to improve SOD model robustness, introducing a similarity measure module, and employing an adversarial learning network to handle labeling uncertainty, the proposed solution achieves state-of-the-art performance for both tasks.