Methods that combine local and global features have recently shown excellent
performance on multiple challenging deep image retrieval benchmarks, but their
use of local features raises at least two issues. First, these local features
simply boil down to the localized map activations of a neural network, and
hence can be extremely redundant. Second, they are typically trained with a
global loss that only acts on top of an aggregation of local features; by
contrast, testing is based on local feature matching, which creates a
discrepancy between training and testing. In this paper, we propose a novel
architecture for deep image retrieval, based solely on mid-level features that
we call Super-features. These Super-features are constructed by an iterative
attention module and constitute an ordered set in which each element focuses on
a localized and discriminant image pattern. For training, they require only
image labels. A contrastive loss operates directly at the level of
Super-features and focuses on those that match across images. A second
complementary loss encourages diversity. Experiments on common landmark
retrieval benchmarks validate that Super-features substantially outperform
state-of-the-art methods when using the same number of features, and only
require a significantly smaller memory footprint to match their performance.
Code and models are available at: this https URL

本文介绍了一种基于超级特征的深度图像检索架构，只需要图像标签即可训练。超级特征由迭代的注意模块构建，通过对 Super-features 实施对比损失来匹配图像。与局部特征相比，这种方法在存储空间方面具有显着优势，并在常见地标检索基准上表现出比现有方法更好的效果。