PaulCouairon/DiffCut
[NeurIPS 2024] Official code for DiffCut: Catalyzing Zero-Shot Semantic Segmentation with Diffusion Features and Recursive Normalized Cut
DiffCut helps automatically outline distinct objects or regions within images, even for categories it hasn't been explicitly trained on. You provide an image, and it outputs a segmented version with different areas highlighted. This tool is useful for researchers and practitioners working in computer vision, autonomous systems, or medical imaging who need to precisely delineate objects in a wide variety of images without extensive manual labeling.
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Use this if you need to precisely segment images into meaningful regions for objects or concepts you haven't pre-defined, saving significant manual annotation time.
Not ideal if you require segmentation of extremely fine-grained details or highly ambiguous objects that even humans struggle to differentiate.
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Jupyter Notebook
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MIT
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Last pushed
Jan 19, 2025
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