AIVResearch/MSANet
Official Pytorch implementation of Multi-Similarity and Attention Guidence for Boosting Few-Shot Segmentation.
This project helps researchers and engineers quickly segment new, unknown objects in images, even when very few examples are available for training. It takes in a few example images with their segmented objects (support images) and a new image (query image), then outputs the precise segmentation mask for the new object. This tool is for those working in computer vision or machine learning who need to perform accurate image segmentation with limited data.
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Use this if you need to identify and outline novel objects in images, where only a handful of labeled examples are available for guidance.
Not ideal if you have a large dataset of pre-labeled images for every object class you want to segment, as more traditional methods might be simpler to implement.
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Python
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Last pushed
Jul 30, 2025
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