Supervised deep learning-based hash and vector quantization are enabling fast
and large-scale image retrieval systems. By fully exploiting label annotations,
they are achieving outstanding retrieval performances compared to the
conventional methods. However, it is painstaking to assign labels precisely for
a vast amount of training data, and also, the annotation process is
error-prone. To tackle these issues, we propose the first deep unsupervised
image retrieval method dubbed Self-supervised Product Quantization (SPQ)
network, which is label-free and trained in a self-supervised manner. We design
a Cross Quantized Contrastive learning strategy that jointly learns codewords
and deep visual descriptors by comparing individually transformed images
(views). Our method analyzes the image contents to extract descriptive
features, allowing us to understand image representations for accurate
retrieval. By conducting extensive experiments on benchmarks, we demonstrate
that the proposed method yields state-of-the-art results even without
supervised pretraining.

提出了 “自我监督产品量化（SPQ）” 网络，它是一个无需标签、自我监督的深度学习图像检索方法，通过比较转换后的图像（视图）来学习深度视觉描述符和码字，分析图像的内容提取其描述性特征，实现准确的图像检索，实验表明在基准测试中得到了业内领先的结果，即使在没有有监督预训练的情况下。