论文标题
半监督图像检索的广义产品量化网络
Generalized Product Quantization Network for Semi-supervised Image Retrieval
论文作者
论文摘要
采用哈希或矢量量化的图像检索方法通过利用深度学习的优势取得了巨大的成功。但是，除非昂贵的标签信息足够，否则这些方法无法满足期望。为了解决此问题，我们提出了第一个基于量化的半监督图像检索方案：广义产品量化（GPQ）网络。我们设计了一种新型的度量学习策略，该策略可保留标记数据之间的语义相似性，并采用熵正则项来充分利用未标记数据的固有潜力。我们的解决方案增加了量化网络的概括能力，从而克服了检索社区的先前限制。广泛的实验结果表明，GPQ在大规模真实图像基准数据集上产生最先进的性能。
Image retrieval methods that employ hashing or vector quantization have achieved great success by taking advantage of deep learning. However, these approaches do not meet expectations unless expensive label information is sufficient. To resolve this issue, we propose the first quantization-based semi-supervised image retrieval scheme: Generalized Product Quantization (GPQ) network. We design a novel metric learning strategy that preserves semantic similarity between labeled data, and employ entropy regularization term to fully exploit inherent potentials of unlabeled data. Our solution increases the generalization capacity of the quantization network, which allows overcoming previous limitations in the retrieval community. Extensive experimental results demonstrate that GPQ yields state-of-the-art performance on large-scale real image benchmark datasets.
