Exploring class-agnostic pixels for scribble-supervised high-resolution salient object detection

Successful salient object detection is largely dependent on large-scale fine-grained annotated datasets. However, pixel-level annotation is a laborious process compared with weak labels and scant research has been done on high-resolution images. To mitigate these drawbacks, we propose a distinctive network to explore salient object in high-resolution images under scribble-supervised and relabel a previous high-resolution dataset with scribbles, namely Scr-HRSOD, in which each image is labelled in a few seconds. Since scribble labels lack structural information about objects, a boundary structure maintenance branch with shallow layers is introduced to capture low-level spatial details. Within the constraint of boundary branches, a lightweight contextual semantic branch process compressed inputs to obtain high-level semantic context and iteratively propagates the partially annotated pixels to surrounding similar regions, which are then employed as pseudo-labels to supervise the network. Extensive evaluations on five datasets illustrate the effectiveness of our introduced method. On HRSOD datasets, we achieve higher 0.861 Fmax and 0.887 Sm values, which outperforms the existing foremost weakly supervised methods and even the fully supervised methods.