Self-supervising Fine-grained Region Similarities for Large-scale Image Localization

• URL: http://arxiv.org/abs/2006.03926v2
• Date: Thu, 9 Jul 2020 06:21:08 GMT
• Title: Self-supervising Fine-grained Region Similarities for Large-scale Image Localization
• Authors: Yixiao Ge, Haibo Wang, Feng Zhu, Rui Zhao, Hongsheng Li
• Abstract summary: General public benchmarks only provide noisy GPS labels for learning image-to-image similarities. We propose to self-supervise image-to-region similarities in order to fully explore the potential of difficult positive images alongside their sub-regions. Our proposed self-enhanced image-to-region similarity labels effectively deal with the training bottleneck in the state-of-the-art pipelines.
• Score: 43.1611420685653
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The task of large-scale retrieval-based image localization is to estimate the geographical location of a query image by recognizing its nearest reference images from a city-scale dataset. However, the general public benchmarks only provide noisy GPS labels associated with the training images, which act as weak supervisions for learning image-to-image similarities. Such label noise prevents deep neural networks from learning discriminative features for accurate localization. To tackle this challenge, we propose to self-supervise image-to-region similarities in order to fully explore the potential of difficult positive images alongside their sub-regions. The estimated image-to-region similarities can serve as extra training supervision for improving the network in generations, which could in turn gradually refine the fine-grained similarities to achieve optimal performance. Our proposed self-enhanced image-to-region similarity labels effectively deal with the training bottleneck in the state-of-the-art pipelines without any additional parameters or manual annotations in both training and inference. Our method outperforms state-of-the-arts on the standard localization benchmarks by noticeable margins and shows excellent generalization capability on multiple image retrieval datasets.

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