Image retrieval methods based on CNN descriptors rely on metric learning from a large number of diverse examples of positive and negative image pairs. Domains, such as night-time images, with limited availability and variability of training data suffer from poor retrieval performance even with methods performing well on standard benchmarks. We propose to train a GAN-based synthetic-image generator, translating available day-time image examples into night images. Such a generator is used in metric learning as a form of augmentation, supplying training data to the scarce domain. Various types of generators are evaluated and analyzed. We contribute with a novel light-weight GAN architecture that enforces the consistency between the original and translated image through edge consistency. The proposed architecture also allows a simultaneous training of an edge detector that operates on both night and day images. To further increase the variability in the training examples and to maximize the generalization of the trained model, we propose a novel method of diverse anchor mining. The proposed method improves over the state-of-the-art results on a standard Tokyo 24/7 day-night retrieval benchmark while preserving the performance on Oxford and Paris datasets. This is achieved without the need of training image pairs of matching day and night images. The source code is available at https://github.com/mohwald/gandtr .

翻译：基于CNN描述符的图像检索方法依赖于从大量多样化的正负图像对样本中进行度量学习。对于夜间图像等训练数据可用性和多样性有限的领域，即使采用在标准基准上表现良好的方法，其检索性能仍较差。本文提出训练一种基于GAN的合成图像生成器，将可用的日间图像样本转换为夜间图像。该生成器在度量学习中充当增强手段，为稀缺领域提供训练数据。我们对多种生成器进行了评估与分析，并提出一种新型轻量级GAN架构，通过边缘一致性约束确保原始图像与转换图像之间的语义一致性。该架构还支持同时训练一个可同时处理夜间与日间图像的边缘检测器。为进一步提升训练样本的多样性并最大化训练模型的泛化能力，我们提出一种新颖的多样化锚点挖掘方法。所提方法在标准东京24/7昼夜检索基准上超越了现有最优结果，同时在牛津和巴黎数据集上保持原有性能。该成果无需匹配日/夜图像对进行训练。源代码见https://github.com/mohwald/gandtr。