CamMem: Structured Camouflage-State Memory Replay with SAM2 for Camouflaged Object Detection

Camouflaged object detection (COD) aims to segment objects concealed by their surroundings, where high foreground–background similarity and indistinct boundaries make accurate perception particularly challenging. Although Segment Anything Model 2 (SAM2) provides strong visual representations and memory mechanisms for video propagation, these mechanisms are not naturally tailored to single-image camouflage perception. In this work, we propose CamMem, a structured camouflage-state memory replay framework that adapts SAM2 to COD through an intra-image memory writing–memory replay–conflict correction process. Specifically, we first introduce Structured Camouflage-State Memory (SCSM), which organizes coarse predictions and visual features into task-specific camouflage states encoding foreground-background, boundary, reliability, and conflict cues. We then develop a Camouflage-State Replay Adapter (CSRA), which combines relation-calibrated state matching, evidence-directed replay, and spatial–channel gated injection to selectively propagate structured camouflage states across SAM2 encoder blocks. Finally, Conflict-Guided Anchor Correction (CGAC) retains the replay-conditioned prediction as the primary hypothesis and uses the initial prediction as a stable anchor, regulating anchor correction through prediction conflict and local-support preference with bounded residual mixing. Experimental results on four COD benchmarks, including CAMO, COD10K, NC4K, and CHAMELEON, show that CamMem achieves substantial performance gains over the SAM2-based baseline and delivers competitive results against state-of-the-art COD methods.