DiCLIP uses diffusion-based visual correlation enhancement and text semantic augmentation to improve CLIP-generated class activation maps for weakly supervised semantic segmentation, outperforming prior methods on PASCAL VOC and MS COCO.
More: Class patch attention needs regularization for weakly supervised semantic segmentation
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Presents APRIL-MedSeg, a modular YAML-configurable toolbox for 2D medical image segmentation integrating semi-supervised, domain adaptation, distillation, weakly supervised, text-guided, and foundation model paradigms with unified dataset and deployment interfaces.
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DiCLIP: Diffusion Model Enhances CLIP's Dense Knowledge for Weakly Supervised Semantic Segmentation
DiCLIP uses diffusion-based visual correlation enhancement and text semantic augmentation to improve CLIP-generated class activation maps for weakly supervised semantic segmentation, outperforming prior methods on PASCAL VOC and MS COCO.
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APRIL-MedSeg: A Modular Medical Image Segmentation Toolbox Embracing Modern Paradigms
Presents APRIL-MedSeg, a modular YAML-configurable toolbox for 2D medical image segmentation integrating semi-supervised, domain adaptation, distillation, weakly supervised, text-guided, and foundation model paradigms with unified dataset and deployment interfaces.