Few-Shot Camouflaged Object Segmentation
Ziqiu Wang, Yuying Li, Yang Yang, Yamin Li, Gaoyang Liu · 2024
In the domain of computer vision, Camouflaged Object Segmentation (COS) is a crucial task aimed at identifying objects that blend into their surroundings, with applications spanning diverse sectors such as military, medical, and beyond. Traditional COS techniques, which primarily depend on supervised learning, necessitate large-scale labeled datasets. However, the acquisition of sufficient camouflage images for such purposes is often constrained due to their scarcity and the high cost of manual annotation. Additionally, these conventional methods frequently struggle to generalize to novel, unseen classes. In response to these challenges, this paper proposes the Camouflage Few-Shot (CAMFS) framework, an innovative approach integrating few-shot learning into COS. The CAMFS framework comprises two main components: the Camouflaged-Meta module, which converts the semantic information of camouflaged objects into compact feature vectors to facilitate knowledge transfer from support to query images; and the Camouflaged-Base module, focused on refining edge detection and enhancing the contrast between foreground and background elements. To overcome the limitations of existing COS datasets, which are primarily designed for supervised learning, we have developed the COS-FSS dataset, the first public few-shot COS dataset. It is based on the COD10K dataset and supplemented with approximately 3200 additional camouflage images. We conducted extensive evaluations of our CAMFS framework on the COS-FSS dataset. Compared to existing COS models, CAMFS demonstrates an average improvement of 5.7% in Sαand 14.02% in $F_\beta ^w$, while against few-shot segmentation models, it achieves a 5.97% increase in m-IoU. The dataset and additional resources are available at https://github.com/CAM-FSS/FSS-COD.