Multi-task information propagation network for camouflaged object detection

Camouflaged object detection (COD) aims to discover camouflaged objects embedded in the background. However, most existing COD methods focus solely on extracting single-task features for inference, and rely on relatively simple supervision data, which hampers their generalization ability in real-world deployment. To meet these challenges, we design a novel camouflaged object detection framework, the Multi-Task Information Propagation Network (MIP-Net). In the feature extraction stage, the Multi-Task Feature Extraction Module (MFEM) learns multi-task feature representations that preserve boundary, texture, and object information to enhance COD performance. During the feature fusion encoding stage, the Cross-Level Multi-Task Fusion Propagation (CMFP) module effectively integrates multi-task features from different levels, adaptively weighting the encoder's features so that the decoder receives more effective information for improved COD outcomes. In the feature decoding inference stage, the Cross-Layer Progressive Refinement Module (CSRM) captures the complementarity of cross-level features, further refining the final inference results. We conducted comprehensive experiments on four benchmark datasets: CHAMELEON, CAMO, COD10K, and NC4K. The comprehensive results prove that the proposed MIP-Net framework outperforms existing methods, showcasing its effectiveness and robustness.