  Existing camouflaged object detection (COD) methods rely heavily on
large-scale datasets with pixel-wise annotations. However, due to the ambiguous
boundary, annotating camouflage objects pixel-wisely is very time-consuming and
labor-intensive, taking ~60mins to label one image. In this paper, we propose
the first weakly-supervised COD method, using scribble annotations as
supervision. To achieve this, we first relabel 4,040 images in existing
camouflaged object datasets with scribbles, which takes ~10s to label one
image. As scribble annotations only describe the primary structure of objects
without details, for the network to learn to localize the boundaries of
camouflaged objects, we propose a novel consistency loss composed of two parts:
a cross-view loss to attain reliable consistency over different images, and an
inside-view loss to maintain consistency inside a single prediction map.
Besides, we observe that humans use semantic information to segment regions
near the boundaries of camouflaged objects. Hence, we further propose a
feature-guided loss, which includes visual features directly extracted from
images and semantically significant features captured by the model. Finally, we
propose a novel network for COD via scribble learning on structural information
and semantic relations. Our network has two novel modules: the local-context
contrasted (LCC) module, which mimics visual inhibition to enhance image
contrast/sharpness and expand the scribbles into potential camouflaged regions,
and the logical semantic relation (LSR) module, which analyzes the semantic
relation to determine the regions representing the camouflaged object.
Experimental results show that our model outperforms relevant SOTA methods on
three COD benchmarks with an average improvement of 11.0% on MAE, 3.2% on
S-measure, 2.5% on E-measure, and 4.4% on weighted F-measure.
