VCP-DCN: Beyond Visual Concealed Property via Depth Collaborative Network for Camouflaged Object Detection

Camouflaged Object Detection (COD) aims to identify andsegment camouflaged objects in complex environments, which are oftenconcealed because their color and texture are similar to the background.Several existing COD methods introduce depth maps to boost detec-tion performance via learning complementary RGB-D features, ignor-ing modality-specific characteristics of concealed objects in the depthdomain. To address this issue, we propose a depth collaborative net-work, called VCP-DCN, to mine distinguishable multi-modality featuresbeyond visual concealed prototype in depth domain. Specifically, VCP-DCN progressively performs multi-modality alignment, interaction, andfusion for the COD task. In the alignment stage, we propose a SeparablePrototype Embedding (SPE) module to learn modality-consistency andmodality-specific RGB/depth prototype tokens through prototype con-trastive learning. Furthermore, we develop a Multi-modality Dual Atten-tion (MDA) module to enhance the cross-modal feature representationthrough local response maps between modality-consistency RGB/depthprototype tokens and visual tokens on the interaction stage. Finally,we design a Depth Adaptive Injection (DAI) module to adaptively mea-sure contribution of RGB/depth features with a decision-making mecha-nism, which calculates similarity distance between RGB/depth modality-specific prototype tokens and modality-consistency ones on the fusionstage. Extensive experiments demonstrate the effectiveness of our VCP-DCN on three authoritative datasets. The code is available at https://github.com/duan-song/VCPDCN.