论文标题
磁盘：通过政策梯度学习本地功能
DISK: Learning local features with policy gradient
论文作者
论文摘要
由于稀疏关键点的选择和匹配所固有的离散性，本地功能框架很难以端到端的方式学习。我们介绍了磁盘（离散关键点），这是一种新颖的方法，通过利用强化学习（RL）的原理来克服这些障碍，以优化端到端，以实现高数量的正确功能匹配。我们简单而表现力的概率模型使我们能够保持训练和推理制度的关闭，同时保持足够好的收敛性能从头开始可靠地训练。如图1所示，我们的功能可以非常密集地提取，同时保持歧视性，具有挑战性的假设，并具有良好的关键点，并在三个公共基准上提供最先进的结果。
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.
