TSegAgent achieves accurate zero-shot tooth segmentation on 3D dental scans via geometry-aware vision-language reasoning without task-specific training.
In: Proceedings of the Computer Vision and Pattern Recognition Con- ference
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DiGSeg repurposes diffusion U-Nets as generalist segmentation learners by conditioning on image-mask latents and multi-scale CLIP text features, achieving strong cross-domain performance.
citing papers explorer
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TSegAgent: Zero-Shot Tooth Segmentation via Geometry-Aware Vision-Language Agents
TSegAgent achieves accurate zero-shot tooth segmentation on 3D dental scans via geometry-aware vision-language reasoning without task-specific training.
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Diffusion Model as a Generalist Segmentation Learner
DiGSeg repurposes diffusion U-Nets as generalist segmentation learners by conditioning on image-mask latents and multi-scale CLIP text features, achieving strong cross-domain performance.