Weakly-Supervised Camouflaged Object Detection with Scribble Annotations
- URL: http://arxiv.org/abs/2207.14083v1
- Date: Thu, 28 Jul 2022 13:40:07 GMT
- Title: Weakly-Supervised Camouflaged Object Detection with Scribble Annotations
- Authors: Ruozhen He and Qihua Dong and Jiaying Lin and Rynson W.H. Lau
- Abstract summary: We propose the first weakly-supervised camouflaged object detection (COD) method, using scribble annotations as supervision.
Annotating camouflage objects pixel-wisely takes 60 minutes per image.
We propose a novel consistency loss composed of two parts: a reliable cross-view loss to attain reliable consistency over different images, and a soft inside-view loss to maintain consistency inside a single prediction map.
- Score: 34.78171563557932
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Existing camouflaged object detection (COD) methods rely heavily on
large-scale datasets with pixel-wise annotations. However, due to the ambiguous
boundary, it is very time-consuming and labor-intensive to annotate camouflage
objects pixel-wisely (which takes ~ 60 minutes per image). In this paper, we
propose the first weakly-supervised camouflaged object detection (COD) method,
using scribble annotations as supervision. To achieve this, we first construct
a scribble-based camouflaged object dataset with 4,040 images and corresponding
scribble annotations. It is worth noting that annotating the scribbles used in
our dataset takes only ~ 10 seconds per image, which is 360 times faster than
per-pixel annotations. However, the network directly using scribble annotations
for supervision will fail to localize the boundary of camouflaged objects and
tend to have inconsistent predictions since scribble annotations only describe
the primary structure of objects without details. To tackle this problem, we
propose a novel consistency loss composed of two parts: a reliable cross-view
loss to attain reliable consistency over different images, and a soft
inside-view loss to maintain consistency inside a single prediction map.
Besides, we observe that humans use semantic information to segment regions
near boundaries of camouflaged objects. Therefore, we design a feature-guided
loss, which includes visual features directly extracted from images and
semantically significant features captured by models. Moreover, we propose a
novel network that detects camouflaged objects by scribble learning on
structural information and semantic relations. Experimental results show that
our model outperforms relevant state-of-the-art 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.
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