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

Boundary-aware Camouflaged Object Detection via Deformable Point Sampling