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• W3037854993 abstract "Local feature frameworks are difficult to learn in an end-to-end fashion, due to the discreteness inherent to the selection and matching of sparse keypoints. We introduce DISK (DIScrete Keypoints), a novel method that overcomes these obstacles by leveraging principles from Reinforcement Learning (RL), optimizing end-to-end for a high number of correct feature matches. Our simple yet expressive probabilistic model lets us keep the training and inference regimes close, while maintaining good enough convergence properties to reliably train from scratch. Our features can be extracted very densely while remaining discriminative, challenging commonly held assumptions about what constitutes a good keypoint, as showcased in Fig. 1, and deliver state-of-the-art results on three public benchmarks." @default.
• W3037854993 created "2020-07-02" @default.
• W3037854993 creator A5038674741 @default.
• W3037854993 creator A5049610895 @default.
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• W3037854993 date "2020-06-24" @default.
• W3037854993 modified "2023-10-01" @default.
• W3037854993 title "DISK: Learning local features with policy gradient" @default.
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• W3037854993 hasPublicationYear "2020" @default.
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