Abstract
Product Quantization (PQ) has long been a mainstream for generating an exponentially large codebook at very low memory/time cost. Despite its
success, PQ is still tricky for the decomposition
of high-dimensional vector space, and the retraining of model is usually unavoidable when the code
length changes. In this work, we propose a deep
progressive quantization (DPQ) model, as an alternative to PQ, for large scale image retrieval.
DPQ learns the quantization codes sequentially and
approximates the original feature space progressively. Therefore, we can train the quantization
codes with different code lengths simultaneously.
Specifically, we first utilize the label information
for guiding the learning of visual features, and
then apply several quantization blocks to progressively approach the visual features. Each quantization block is designed to be a layer of a convolutional neural network, and the whole framework
can be trained in an end-to-end manner. Experimental results on the benchmark datasets show that
our model significantly outperforms the state-ofthe-art for image retrieval. Our model is trained
once for different code lengths and therefore requires less computation time. Additional ablation
study demonstrates the effect of each component
of our proposed model. Our code is released at
https://github.com/cfm-uestc/DPQ