Use 'semantic gradients' to turn vocabulary study into a shared thinking activity that explores the subtle differences ...
A recent study by the Laboratory for Neuro-Analysis and Imaging (LANAI) at UMass Chan Medical School highlights how ...
Abstract: This paper introduces Clip2Sam, a cutting-edge end-to-end text-to-image automatic segmentation system that synergizes the strengths of CLIP and SAM models to redefine state-of-the-art ...
A new study presents a zero-shot learning (ZSL) framework for maize cob phenotyping, enabling the extraction of geometric ...
Unsupervised domain adaptation (UDA) involves learning class semantics from labeled data within a source domain that generalize to an unseen target domain. UDA methods are particularly impactful for ...
dragon-shaped asset where scales, horns, and whiskers follow the underlying geometry rather than appearing as a flat overlay.
SAM3 ~160MB Auto-segment/Text 0.3-0.7s N/A SAM2.1_T (CPU) ~40MB Fast CPU N/A <1s SAM2.1_B (CPU) ~80MB Balanced CPU N/A 1-2s SAM2.1_L (CPU) ~224MB Quality CPU N/A 2-3s Select a class (Buildings, Water, ...
Abstract: Existing surface defect semantic segmentation methods are limited by costly annotated data and are unable to cope with new or rare defect types. Zero-shot learning offers a new possibility ...
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