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Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.

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NAF: Zero-Shot Feature Upsampling via Neighborhood Attention Filtering

📝 Summary:
NAF upsamples Vision Foundation Model features zero-shot by learning adaptive spatial-and-content weights. It outperforms VFM-specific upsamplers without retraining, achieving state-of-the-art performance across various tasks efficiently.

🔹 Publication Date: Published on Nov 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.18452
• PDF: https://arxiv.org/pdf/2511.18452
• Github: https://github.com/valeoai/NAF?tab=readme-ov-file

==================================

For more data science resources:
https://t.me/DataScienceT

#ZeroShotLearning #ComputerVision #FeatureUpsampling #DeepLearning #AIResearch
This media is not supported in your browser
VIEW IN TELEGRAM
NAF: Zero-Shot Feature Upsampling via Neighborhood Attention Filtering

📝 Summary:
NAF upsamples Vision Foundation Model features zero-shot by learning adaptive spatial-and-content weights. It outperforms VFM-specific upsamplers without retraining, achieving state-of-the-art performance across various tasks efficiently.

🔹 Publication Date: Published on Nov 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.18452
• PDF: https://arxiv.org/pdf/2511.18452
• Github: https://github.com/valeoai/NAF?tab=readme-ov-file

==================================

For more data science resources:
https://t.me/DataScienceT

#ZeroShotLearning #ComputerVision #FeatureUpsampling #DeepLearning #AIResearch