论文标题
MTLDESC：看起来更广泛地描述更好
MTLDesc: Looking Wider to Describe Better
论文作者
论文摘要
受卷积神经网络的局部性的限制，大多数现有的本地功能描述方法仅学习具有本地信息的本地描述符，并且缺乏对全球和周围空间环境的认识。在这项工作中，我们专注于通过学习本地描述符不仅仅是本地信息（MTLDESC）来使本地描述符“看起来更广泛地描述更好”。具体而言，我们诉诸于上下文增强和空间注意机制，以使我们的MTLDESC获得非本地意识。首先，提出了自适应的全球环境增强模块和多样化的本地环境增强模块，以构建具有从全球到本地的上下文信息的强大的本地描述符。其次，一致的关注加权三重损失旨在将空间注意力的意识整合到本地描述符学习的优化和匹配阶段。第三，给出了具有特征金字塔的局部特征检测，以获得更稳定和准确的关键点的定位。通过上述创新，我们的MTLDESC的性能显着超过了先前的HPATCHES，AACHEN DAY NIT NITION和INLOC室内定位基准的本地描述符。
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.
