This work focuses on mitigating two limitations in the joint learning of
local feature detectors and descriptors. First, the ability to estimate the
local shape (scale, orientation, etc.) of feature points is often neglected
during dense feature extraction, while the shape-awareness is crucial to
acquire stronger geometric invariance. Second, the localization accuracy of
detected keypoints is not sufficient to reliably recover camera geometry, which
has become the bottleneck in tasks such as 3D reconstruction. In this paper, we
present ASLFeat, with three light-weight yet effective modifications to
mitigate above issues. First, we resort to deformable convolutional networks to
densely estimate and apply local transformation. Second, we take advantage of
the inherent feature hierarchy to restore spatial resolution and low-level
details for accurate keypoint localization. Finally, we use a peakiness
measurement to relate feature responses and derive more indicative detection
scores. The effect of each modification is thoroughly studied, and the
evaluation is extensively conducted across a variety of practical scenarios.
State-of-the-art results are reported that demonstrate the superiority of our
methods.

ASLFeat 是一种新的本地特征检测器和描述符结合的方法，使用可变形卷积网络估计本地特征点的形状，从而实现更强的几何不变性。此外，ASLFeat 使用层次结构以恢复空间分辨率和局部细节，并通过尖锐度测量来推导最有指示性的检测分数，以进一步提高检测精度和可靠性，并可在各种实际场景中获得最新的研究成果。

ASLFeat: 学习准确形状和定位的本地特征

ASLFeat: Learning Local Features of Accurate Shape and Localization

This paper provides an extensive study on the availability of image
representations based on convolutional networks (ConvNets) for the task of
visual instance retrieval. Besides the choice of convolutional layers, we
present an efficient pipeline exploiting multi-scale schemes to extract local
features, in particular, by taking geometric invariance into explicit account,
i.e. positions, scales and spatial consistency. In our experiments using five
standard image retrieval datasets, we demonstrate that generic ConvNet image
representations can outperform other state-of-the-art methods if they are
extracted appropriately.

本文研究使用卷积网络所提供的图像表示在视觉实例检索任务中的可用性，并通过利用多尺度图像特征提取方案，特别地，通过将几何不变性纳入到明确的位置、尺度和空间一致性中的方法来提取来自于该网络的局部特征，实验结果表明，如果对其进行适当提取，那么通用卷积网络图像表示法可优于其他先进方法。