Weakly-supervised Camouflaged Object Detection via SAM-guided Resolution Iteration Learning

Weakly supervised camouflaged object detection (WS-COD) aims to address the critical task of identifying visually assimilated objects concealed within heterogeneous backgrounds under sparse supervisory signals. However, current WS-COD frameworks suffer from compromised structural integrity, stemming from cross-hierarchical feature discrepancy and constrained cross-level information flow, which induces structural misalignment and context fragmentation in multi-granularity feature fusion. To overcome the limitation, we propose a novel SAM-guided Resolution Iteration Learning Network (SAM-RNet) that synergizes foundation model priors with multi-resolution feature refinement. Our technical contributions are threefold: (1) We utilize the Segment Anything Model (SAM) to produce high-quality masks, effectively mitigating supervision insufficiency through large-scale visual knowledge distillation. (2) We design a resolution iteration mechanism where high-resolution features progressively refine low-resolution counterparts through an Interactive Refinement Module (IRM) - a dual-branch architecture enabling hierarchical feature interaction and enhancement through branch collaboration and attention mechanism, complemented by an iterative feedback loss to enforce multi-scale feature learning. (3) We develop a Decoder with cross-layer fusion operations, enabling the aggregation of features from object and background contexts for precise object segmentation. Finally, extensive experiments demonstrate that SAM-RNet is superior to existing WS-COD methods across three COD datasets, achieving average improvements of 4.37%, 4.60%, 7.00%, and 24.06% in $S_{\alpha }$, $E_{\phi }$, $F_{\beta }^{\omega }$, and $M$, respectively.