  Quantization has been an effective technology in ANN (approximate nearest
neighbour) search due to its high accuracy and fast search speed. To meet the
requirement of different applications, there is always a trade-off between
retrieval accuracy and speed, reflected by variable code lengths. However, to
encode the dataset into different code lengths, existing methods need to train
several models, where each model can only produce a specific code length. This
incurs a considerable training time cost, and largely reduces the flexibility
of quantization methods to be deployed in real applications. To address this
issue, we propose a Deep Recurrent Quantization (DRQ) architecture which can
generate sequential binary codes. To the end, when the model is trained, a
sequence of binary codes can be generated and the code length can be easily
controlled by adjusting the number of recurrent iterations. A shared codebook
and a scalar factor is designed to be the learnable weights in the deep
recurrent quantization block, and the whole framework can be trained in an
end-to-end manner. As far as we know, this is the first quantization method
that can be trained once and generate sequential binary codes. Experimental
results on the benchmark datasets show that our model achieves comparable or
even better performance compared with the state-of-the-art for image retrieval.
But it requires significantly less number of parameters and training times. Our
code is published online: https://github.com/cfm-uestc/DRQ.
