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.
In: Proceedings of the Computer Vision and Pattern Recognition Con- ference
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TSegAgent performs zero-shot tooth instance segmentation and identification on 3D dental scans via multi-view foundation models plus explicit dental-arch geometric reasoning.
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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.
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TSegAgent: Zero-Shot Tooth Segmentation via Geometry-Aware Vision-Language Agents
TSegAgent performs zero-shot tooth instance segmentation and identification on 3D dental scans via multi-view foundation models plus explicit dental-arch geometric reasoning.
- LoCA: Spatially-Aware Low-Rank Convolutional Adaptation of Vision Foundation Models