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GDI transforms defect classification by generating single-defect samples, leading to a remarkable 63.6% boost in F1-Score for rare defects.
Robustness in Open Vocabulary Object Detectors is driven more by image domain characteristics than by annotation methods, challenging existing assumptions about model training.
Gradient-trained models struggle with unseen bacterial combinations, but lightweight anchor-based decoders can significantly improve identification accuracy in open-world scenarios.
You can now estimate grain size in microscopy images with surprising accuracy (1.5% MAPE) using just two training samples, thanks to a clever adaptation of Cellpose-SAM.
