ST-SAM combines self-training with entropy-based pseudo-label filtering and SAM prompt-based mutual correction to achieve strong camouflaged object detection with only 1 percent labeled data.
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ST-SAM: SAM-Driven Self-Training Framework for Semi-Supervised Camouflaged Object Detection
ST-SAM combines self-training with entropy-based pseudo-label filtering and SAM prompt-based mutual correction to achieve strong camouflaged object detection with only 1 percent labeled data.