TL;DR提出了一种名为“backward-compatible training (BCT)”的框架来训练嵌入模型,它可使不同维度和架构、通过不同损失函数学习的视觉特征具有兼容性,从而实现新计算机制与旧计算机制之间的互换,使视觉搜索系统能够跳过计算所有以前看到的图像的新功能。
Abstract
We propose a way to learn visual features that are compatible with previously computed ones even when they have different dimensions and are learned via different neural network architectures and →