Hierarchically Aggregated Identification Transformer Network for Camouflaged Object Detection

Camouflaged object detection (COD) targets the segmentation of objects hidden in intricate environments, a task complicated by the pronounced similarities between objects and their surroundings. The diverse appearances of camouflaged objects, such as different view angles, partial visibilities, and ambiguous forms, further exacerbate this challenge. To address these issues, we introduce the Hierarchically Aggregated Identification Transformer Network (HAIT-Net). HAITNet harnesses local and global features to refine object localization by employing multi-scale transformer features unified through the Feature Cascaded Fusion Module (FCFM). To tackle ambiguity from indistinct textures, we present the Graph-based Low-level Feature Enhancement Module (GLFEM) and Graph-based Feature Aggregation Module (GFAM). GLFEM enhances texture representation in ambiguous areas, while GFAM reduces false positives and refines prediction maps by discerning contextual relationships. Experimental results on three widely used datasets demonstrate that the proposed HAITNet outperforms the state-of-the-art approaches. Our code is available at https://github.com/underlmao/HAITNet.