Image retrieval can be formulated as a ranking problem where the goal is to
order database images by decreasing similarity to the query. Recent deep models
for image retrieval have outperformed traditional methods by leveraging
ranking-tailored loss functions, but important theoretical and practical
problems remain. First, rather than directly optimizing the global ranking,
they minimize an upper-bound on the essential loss, which does not necessarily
result in an optimal mean average precision (mAP). Second, these methods
require significant engineering efforts to work well, e.g. special pre-training
and hard-negative mining. In this paper we propose instead to directly optimize
the global mAP by leveraging recent advances in listwise loss formulations.
Using a histogram binning approximation, the AP can be differentiated and thus
employed to end-to-end learning. Compared to existing losses, the proposed
method considers thousands of images simultaneously at each iteration and
eliminates the need for ad hoc tricks. It also establishes a new state of the
art on many standard retrieval benchmarks. Models and evaluation scripts have
been made available at this https URL

本研究借助最近的榜单损失函数理论和实践证明，通过直接优化全局平均精度，提出了一个图像检索模型，消除了现有模型所需的工程努力和基于工具性方法的需求，并在许多标准检索基准测试中建立了新的基准。