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<li><strong>Repo:</strong> <a href="https://github.com/PictSure/pictsure-library">PictSure on GitHub</a></li>
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<li><strong>Paper:</strong> <a href="https://arxiv.org/abs/2506.14842">arXiv:2506.14842</a></li>
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<h3>๐ Summary</h3>
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PictSure is a lightweight vision-only in-context learning (ICL) family of models for few-shot image classification (FSIC).
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It uses pretrained frozen
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<h3>๐ Highlights</h3>
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<li>No fine-tuning required</li>
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<li>Purely visual embeddings</li>
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<li>Strong out-of-domain generalization</li>
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<p><strong>License:</strong> MIT</p>
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<strong>Contacts:</strong>
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<a href="mailto:lukas.schiesser@dfki.de">lukas.schiesser@dfki.de</a>
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<a href="mailto:cornelius.wolff@cwi.nl">cornelius.wolff@cwi.nl</a>
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</p>
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<h1>PictSure Model Family</h1>
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<li><strong>Repo:</strong> <a href="https://github.com/PictSure/pictsure-library">PictSure on GitHub</a></li>
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<li><strong>Paper:</strong> <a href="https://arxiv.org/abs/2506.14842">arXiv:2506.14842</a></li>
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<h3>๐ Summary</h3>
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<p>
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PictSure is a lightweight vision-only in-context learning (ICL) family of models for few-shot image classification (FSIC).
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It uses pretrained frozen embeddings and outperforms CLIP-based models on out-of-domain tasks like medical imaging.
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</p>
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<p>
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<strong>Contacts:</strong>
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<a href="mailto:lukas.schiesser@dfki.de">lukas.schiesser@dfki.de</a> &
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<a href="mailto:cornelius.wolff@cwi.nl">cornelius.wolff@cwi.nl</a>
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</p>
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