  Camouflaged object detection and segmentation is a new and challenging
research topic in computer vision. There is a serious issue of lacking data on
concealed objects such as camouflaged animals in natural scenes. In this paper,
we address the problem of few-shot learning for camouflaged object detection
and segmentation. To this end, we first collect a new dataset, CAMO-FS, for the
benchmark. As camouflaged instances are challenging to recognize due to their
similarity compared to the surroundings, we guide our models to obtain
camouflaged features that highly distinguish the instances from the background.
In this work, we propose FS-CDIS, a framework to efficiently detect and segment
camouflaged instances via two loss functions contributing to the training
process. Firstly, the instance triplet loss with the characteristic of
differentiating the anchor, which is the mean of all camouflaged foreground
points, and the background points are employed to work at the instance level.
Secondly, to consolidate the generalization at the class level, we present
instance memory storage with the scope of storing camouflaged features of the
same category, allowing the model to capture further class-level information
during the learning process. The extensive experiments demonstrated that our
proposed method achieves state-of-the-art performance on the newly collected
dataset. Code is available at https://github.com/danhntd/FS-CDIS.
