Joint Salient Object Detection and Camouflaged Object Detection via
  Uncertainty-aware Learning
Aixuan Li Jing Zhang Yunqiu Lv Tong Zhang Yiran Zhong Mingyi He Yuchao Dai
Salient objects attract human attention and usually stand out clearly from
their surroundings. In contrast, camouflaged objects share similar colors or
textures with the environment. In this case, salient objects are typically
non-camouflaged, and camouflaged objects are usually not salient. Due to this
inherent contradictory attribute, we introduce an uncertainty-aware learning
pipeline to extensively explore the contradictory information of salient object
detection (SOD) and camouflaged object detection (COD) via data-level and
task-wise contradiction modeling. We first exploit the dataset correlation of
these two tasks and claim that the easy samples in the COD dataset can serve as
hard samples for SOD to improve the robustness of the SOD model. Based on the
assumption that these two models should lead to activation maps highlighting
different regions of the same input image, we further introduce a contrastive
module with a joint-task contrastive learning framework to explicitly model the
contradictory attributes of these two tasks. Different from conventional
intra-task contrastive learning for unsupervised representation learning, our
contrastive module is designed to model the task-wise correlation, leading to
cross-task representation learning. To better understand the two tasks from the
perspective of uncertainty, we extensively investigate the uncertainty
estimation techniques for modeling the main uncertainties of the two tasks,
namely task uncertainty (for SOD) and data uncertainty (for COD), and aiming to
effectively estimate the challenging regions for each task to achieve
difficulty-aware learning. Experimental results on benchmark datasets
demonstrate that our solution leads to both state-of-the-art performance and
informative uncertainty estimation.