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Segment object into parts unsupervised using knowledge inside stable diffusion using with EmerDiff

Segment object into parts unsupervised using knowledge inside stable diffusion using with EmerDiff


EmerDiff: Emerging Pixel-level Semantic Knowledge in Diffusion Models



Diffusion models have recently received increasing research attention for their remarkable transfer abilities in semantic segmentation tasks.


... generating fine-grained segmentation masks with diffusion models ... requires ... training on annotated datasets ... unclear to what extent pre-trained diffusion models ... understand the semantic relations


... leverage the semantic knowledge extracted from Stable Diffusion (SD) and aim to develop an image segmentor capable of generating fine-grained segmentation maps without any additional training.


... difficulty stems from ... semantically meaningful feature maps typically exist only in the spatially lower-dimensional layers, which poses a challenge in directly extracting pixel-level semantic relations


... framework identifies semantic correspondences between image pixels and spatial locations of low-dimensional feature maps by exploiting SD's generation process and utilizes them for constructing image-resolution segmentation maps.


... segmentation maps are demonstrated to be well delineated and capture detailed parts of the images, indicating the existence of highly accurate pixel-level semantic knowledge in diffusion models.



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