  The camouflaged object detection (COD) task aims to identify and segment
objects that blend into the background due to their similar color or texture.
Despite the inherent difficulties of the task, COD has gained considerable
attention in several fields, such as medicine, life-saving, and anti-military
fields. In this paper, we propose a novel solution called the Deformable Point
Sampling network (DPS-Net) to address the challenges associated with COD. The
proposed DPS-Net utilizes a Deformable Point Sampling transformer (DPS
transformer) that can effectively capture sparse local boundary information of
significant object boundaries in COD using a deformable point sampling method.
Moreover, the DPS transformer demonstrates robust COD performance by extracting
contextual features for target object localization through integrating rough
global positional information of objects with boundary local information. We
evaluate our method on three prominent datasets and achieve state-of-the-art
performance. Our results demonstrate the effectiveness of the proposed method
through comparative experiments.
