  Existing Camouflaged Object Detection (COD) methods rely heavily on
large-scale pixel-annotated training sets, which are both time-consuming and
labor-intensive. Although weakly supervised methods offer higher annotation
efficiency, their performance is far behind due to the unclear visual
demarcations between foreground and background in camouflaged images. In this
paper, we explore the potential of using boxes as prompts in camouflaged scenes
and introduce the first weakly semi-supervised COD method, aiming for
budget-efficient and high-precision camouflaged object segmentation with an
extremely limited number of fully labeled images. Critically, learning from
such limited set inevitably generates pseudo labels with serious noisy pixels.
To address this, we propose a noise correction loss that facilitates the
model's learning of correct pixels in the early learning stage, and corrects
the error risk gradients dominated by noisy pixels in the memorization stage,
ultimately achieving accurate segmentation of camouflaged objects from noisy
labels. When using only 20% of fully labeled data, our method shows superior
performance over the state-of-the-art methods.
