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Исследовательская разработка (Research Preview), представляющая собой фреймворк для параметро-эффективной доменной адаптации модели Segment Anything Model (SAM) под задачи сегментации внутренних дефектов (пористость, трещины) в данных рентгеновской компьютерной томографии (XCT). Архитектура использует метод сверточной низкоранговой адаптации (Conv-LoRA) внедренный в энкодер ViT-H, что позволяет сохранять пространственные признаки при обучении на малых выборках. Модель применяет стратегию последовательной адаптации (Sequential Domain Adaptation): первичная настройка на синтетических данных (CycleGAN-XCT) с последующей донастройкой на реальных промышленных сканах (NIST).
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Snapshot received: 9 Aug 2026, 20:01 UTC.