CM-TTA adapts SAM3 for medical segmentation at test time via semantic consistency-based augmentation selection and dual long-short prompt memory for stable pseudo-label generation.
International Journal of Computer Vision133(1), 31–64 (2025)
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A heterogeneous multi-agent framework with Bayesian test-time orchestration improves spatial reasoning across four benchmarks without parameter updates.
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Concept Alignment Contrast and Long-Short Prompt Memory for Test-Time Adaptation of SAM3 in Medical Image Segmentation
CM-TTA adapts SAM3 for medical segmentation at test time via semantic consistency-based augmentation selection and dual long-short prompt memory for stable pseudo-label generation.
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A heterogeneous multi-agent framework with Bayesian test-time orchestration improves spatial reasoning across four benchmarks without parameter updates.