We propose a novel learned keypoint detection method to increase the number
of correct matches for the task of non-rigid image correspondence. By
leveraging true correspondences acquired by matching annotated image pairs with
a specified descriptor extractor, we train an end-to-end convolutional neural
network (CNN) to find keypoint locations that are more appropriate to the
considered descriptor. For that, we apply geometric and photometric warpings to
images to generate a supervisory signal, allowing the optimization of the
detector. Experiments demonstrate that our method enhances the Mean Matching
Accuracy of numerous descriptors when used in conjunction with our detection
method, while outperforming the state-of-the-art keypoint detectors on real
images of non-rigid objects by 20 p.p. We also apply our method on the complex
real-world task of object retrieval where our detector performs on par with the
finest keypoint detectors currently available for this task. The source code
and trained models are publicly available at
this https URL

我们提出了一种新颖的学习关键点检测方法，用于增加非刚性图像对应任务中正确匹配数目。通过利用使用指定描述符提取器匹配注释图像对获得的真实对应关系，我们训练了一个端到端的卷积神经网络（CNN）来寻找更适合所考虑描述符的关键点位置。为此，我们对图像应用几何和光度变换来生成监督信号，从而优化检测器。实验证明，我们的方法在与我们的检测方法结合使用时，可以提高多个描述符的均值匹配精度，并且在非刚性对象的真实图像上优于最先进的关键点检测器 20 个百分点。我们还将我们的方法应用于复杂的现实世界的物体检索任务中，我们的检测器在这个任务上的性能与当前可用的最佳关键点检测器相当。源代码和训练好的模型可以在此 URL 上公开获取。

学习检测关键点以改善可变形对象的匹配

Improving the matching of deformable objects by learning to detect  keypoints

A novel image matching method is proposed that utilizes learned features
extracted by an off-the-shelf deep neural network to obtain a promising
performance. The proposed method uses pre-trained VGG architecture as a feature
extractor and does not require any additional training specific to improve
matching. Inspired by well-established concepts in the psychology area, such as
the Mental Rotation paradigm, an initial warping is performed as a result of a
preliminary geometric transformation estimate. These estimates are simply based
on dense matching of nearest neighbors at the terminal layer of VGG network
outputs of the images to be matched. After this initial alignment, the same
approach is repeated again between reference and aligned images in a
hierarchical manner to reach a good localization and matching performance. Our
algorithm achieves 0.57 and 0.80 overall scores in terms of Mean Matching
Accuracy (MMA) for 1 pixel and 2 pixels thresholds respectively on Hpatches
dataset, which indicates a better performance than the state-of-the-art.

本文提出了一种新颖的图像匹配方法，利用预先训练的 VGG 神经网络提取图像特征，在首次较好的几何转换估计后，再以分层的方式重复运用该方法，达到良好的本地化和匹配性能。该算法在 Hpatches 数据集上达到了 0.57 和 0.80 的平均匹配准确性分数，表现优于现有技术。