Local covariant feature detection, namely the problem of extracting viewpoint
invariant features from images, has so far largely resisted the application of
machine learning techniques. In this paper, we propose the first fully general
formulation for learning local covariant feature detectors. We propose to cast
detection as a regression problem, enabling the use of powerful regressors such
as deep neural networks. We then derive a covariance constraint that can be
used to automatically learn which visual structures provide stable anchors for
local feature detection. We support these ideas theoretically, proposing a
novel analysis of local features in term of geometric transformations, and we
show that all common and many uncommon detectors can be derived in this
framework. Finally, we present empirical results on translation and rotation
covariant detectors on standard feature benchmarks, showing the power and
flexibility of the framework.

本文提出了第一个完全通用的局部协变特征检测器学习方法，将检测视为回归问题，使用深度神经网络等强大的回归器，提出了一个可以用于自动学习提供局部特征检测稳定锚点的协方差约束，并在标准特征基准测试中展示了平移和旋转协变检测器的实证结果。

学习协变特征检测器

Learning Covariant Feature Detectors

Invariance to geometric transformations is a highly desirable property of
automatic classifiers in many image recognition tasks. Nevertheless, it is
unclear to which extent state-of-the-art classifiers are invariant to basic
transformations such as rotations and translations. This is mainly due to the
lack of general methods that properly measure such an invariance. In this
paper, we propose a rigorous and systematic approach for quantifying the
invariance to geometric transformations of any classifier. Our key idea is to
cast the problem of assessing a classifier's invariance as the computation of
geodesics along the manifold of transformed images. We propose the Manitest
method, built on the efficient Fast Marching algorithm to compute the
invariance of classifiers. Our new method quantifies in particular the
importance of data augmentation for learning invariance from data, and the
increased invariance of convolutional neural networks with depth. We foresee
that the proposed generic tool for measuring invariance to a large class of
geometric transformations and arbitrary classifiers will have many applications
for evaluating and comparing classifiers based on their invariance, and help
improving the invariance of existing classifiers.

本文提出了一种严谨和系统的方法来量化任何分类器对几何变换的不变性，并使用该方法证明了数据增强对学习不变性的重要性，以及卷积神经网络深度增加时不变性的增加。该方法可应用于评估和比较分类器的不变性，并有助于改进现有分类器的不变性。

Manitest：分类器真的具有不变性吗？

Manitest: Are classifiers really invariant?

Convolutional Neural Network (CNN) features have been successfully employed
in recent works as an image descriptor for various vision tasks. But the
inability of the deep CNN features to exhibit invariance to geometric
transformations and object compositions poses a great challenge for image
search. In this work, we demonstrate the effectiveness of the objectness prior
over the deep CNN features of image regions for obtaining an invariant image
representation. The proposed approach represents the image as a vector of
pooled CNN features describing the underlying objects. This representation
provides robustness to spatial layout of the objects in the scene and achieves
invariance to general geometric transformations, such as translation, rotation
and scaling. The proposed approach also leads to a compact representation of
the scene, making each image occupy a smaller memory footprint. Experiments
show that the proposed representation achieves state of the art retrieval
results on a set of challenging benchmark image datasets, while maintaining a
compact representation.

本文提出一种基于卷积神经网络和物件性先验知识的图像检索方法，通过对图像区域深度特征和物件性先验的结合，有效地解决了图像在几何变换和物品构成方面的不变性问题，从而实现更准确高效的图像检索。