  In this paper, we introduce a novel multimodal camo-perceptive framework
(MMCPF) aimed at handling zero-shot Camouflaged Object Detection (COD) by
leveraging the powerful capabilities of Multimodal Large Language Models
(MLLMs). Recognizing the inherent limitations of current COD methodologies,
which predominantly rely on supervised learning models demanding extensive and
accurately annotated datasets, resulting in weak generalization, our research
proposes a zero-shot MMCPF that circumvents these challenges. Although MLLMs
hold significant potential for broad applications, their effectiveness in COD
is hindered and they would make misinterpretations of camouflaged objects. To
address this challenge, we further propose a strategic enhancement called the
Chain of Visual Perception (CoVP), which significantly improves the perceptual
capabilities of MLLMs in camouflaged scenes by leveraging both linguistic and
visual cues more effectively. We validate the effectiveness of MMCPF on five
widely used COD datasets, containing CAMO, COD10K, NC4K, MoCA-Mask and OVCamo.
Experiments show that MMCPF can outperform all existing state-of-the-art
zero-shot COD methods, and achieve competitive performance compared to
weakly-supervised and fully-supervised methods, which demonstrates the
potential of MMCPF. The Github link of this paper is
\url{https://github.com/luckybird1994/MMCPF}.
