  Limited by the locality of convolutional neural networks, most existing local
features description methods only learn local descriptors with local
information and lack awareness of global and surrounding spatial context. In
this work, we focus on making local descriptors "look wider to describe better"
by learning local Descriptors with More Than just Local information (MTLDesc).
Specifically, we resort to context augmentation and spatial attention
mechanisms to make our MTLDesc obtain non-local awareness. First, Adaptive
Global Context Augmented Module and Diverse Local Context Augmented Module are
proposed to construct robust local descriptors with context information from
global to local. Second, Consistent Attention Weighted Triplet Loss is designed
to integrate spatial attention awareness into both optimization and matching
stages of local descriptors learning. Third, Local Features Detection with
Feature Pyramid is given to obtain more stable and accurate keypoints
localization. With the above innovations, the performance of our MTLDesc
significantly surpasses the prior state-of-the-art local descriptors on
HPatches, Aachen Day-Night localization and InLoc indoor localization
benchmarks.
