Tarot-SAM3 delivers a training-free pipeline for segmenting images from arbitrary referring expressions via expression reasoning prompts and DINOv3-based mask self-refinement.
Medical sam3: A foundation model for universal prompt-driven medical image segmen- tation
4 Pith papers cite this work. Polarity classification is still indexing.
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RGSUD achieves SOTA unsupervised deraining by using IQA-based reward recycling and self-reinforcement to constrain optimization and improve pseudo-paired data quality.
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.
DINO-Med3D progressively adapts DINOv3 for 3D medical segmentation via multi-slice embedding, segmentation proxy, 3D adapters, and parallel detail recovery, outperforming baselines on five public datasets.
citing papers explorer
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Tarot-SAM3: Training-free SAM3 for Any Referring Expression Segmentation
Tarot-SAM3 delivers a training-free pipeline for segmenting images from arbitrary referring expressions via expression reasoning prompts and DINOv3-based mask self-refinement.
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Unpaired Image Deraining Using Reward-Guided Self-Reinforcement Strategy
RGSUD achieves SOTA unsupervised deraining by using IQA-based reward recycling and self-reinforcement to constrain optimization and improve pseudo-paired data quality.
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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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DINO-Med3D: Bridging Dimension and Domain Gaps in Volumetric Segmentation via Progressive Adaptation
DINO-Med3D progressively adapts DINOv3 for 3D medical segmentation via multi-slice embedding, segmentation proxy, 3D adapters, and parallel detail recovery, outperforming baselines on five public datasets.