  Visually detecting camouflaged objects is a hard problem for both humans and
computer vision algorithms. Strong similarities between object and background
appearance make the task significantly more challenging than traditional object
detection or segmentation tasks. Current state-of-the-art models use either
convolutional neural networks or vision transformers as feature extractors.
They are trained in a fully supervised manner and thus need a large amount of
labeled training data. In this paper, both self-supervised and frugal learning
methods are introduced to the task of Camouflaged Object Detection (COD). The
overall goal is to fine-tune two COD reference methods, namely SINet-V2 and
HitNet, pre-trained for camouflaged animal detection to the task of camouflaged
human detection. Therefore, we use the public dataset CPD1K that contains
camouflaged humans in a forest environment. We create a strong baseline using
supervised frugal transfer learning for the fine-tuning task. Then, we analyze
three pseudo-labeling approaches to perform the fine-tuning task in a
self-supervised manner. Our experiments show that we achieve similar
performance by pure self-supervision compared to fully supervised frugal
learning.
