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-of-the-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 this https URL

该论文介绍了一种用于大规模图像检索的深度渐进量化模型，该模型学习逐步逼近视觉特征的量化码，并且可以同时训练不同码长的量化码，实现了优于现有技术的图像检索效果。

超越产品量化：深度渐进量化用于图像检索

Beyond Product Quantization: Deep Progressive Quantization for Image  Retrieval

This paper addresses the problem of large scale image retrieval, with the aim
of accurately ranking the similarity of a large number of images to a given
query image. To achieve this, we propose a novel Siamese network. This network
consists of two computational strands, each comprising of a CNN component
followed by a Fisher vector component. The CNN component produces dense, deep
convolutional descriptors that are then aggregated by the Fisher Vector method.
Crucially, we propose to simultaneously learn both the CNN filter weights and
Fisher Vector model parameters. This allows us to account for the evolving
distribution of deep descriptors over the course of the learning process. We
show that the proposed approach gives significant improvements over the
state-of-the-art methods on the Oxford and Paris image retrieval datasets.
Additionally, we provide a baseline performance measure for both these datasets
with the inclusion of 1 million distractors.

本文介绍了一种用于大规模图像检索的新型 Siamese 网络，结合了 CNN 组件和 Fisher vector 组件，可以同时学习 CNN 过滤器权重和 Fisher Vector 模型参数，并且结果比现有的方法有显著改善。