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Found 2 papers, 1 papers with code
LucidPPN: Unambiguous Prototypical Parts Network for User-centric Interpretable Computer Vision
Prototypical parts networks combine the power of deep learning with the explainability of case-based reasoning to make accurate, interpretable decisions.
TORE: Token Recycling in Vision Transformers for Efficient Active Visual Exploration
Active Visual Exploration (AVE) optimizes the utilization of robotic resources in real-world scenarios by sequentially selecting the most informative observations.
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