Beyond Product Quantization: Deep Progressive Quantization for Image Retrieval

06/16/2019
by   Lianli Gao, et al.
13

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 https://github.com/cfm-uestc/DPQ.

READ FULL TEXT

page 1

page 2

page 3

page 4

page 5

page 6

page 7

research
06/16/2019

Deep Recurrent Quantization for Generating Sequential Binary Codes

Quantization has been an effective technology in ANN (approximate neares...
research
07/12/2018

Learning Product Codebooks using Vector Quantized Autoencoders for Image Retrieval

The Vector Quantized-Variational Autoencoder (VQ-VAE) provides an unsupe...
research
03/15/2016

Scalable Image Retrieval by Sparse Product Quantization

Fast Approximate Nearest Neighbor (ANN) search technique for high-dimens...
research
09/06/2021

Self-supervised Product Quantization for Deep Unsupervised Image Retrieval

Supervised deep learning-based hash and vector quantization are enabling...
research
06/20/2022

Self-Supervised Consistent Quantization for Fully Unsupervised Image Retrieval

Unsupervised image retrieval aims to learn an efficient retrieval system...
research
01/30/2020

Optimized Feature Space Learning for Generating Efficient Binary Codes for Image Retrieval

In this paper we propose an approach for learning low dimensional optimi...
research
03/15/2022

Implicit Feature Decoupling with Depthwise Quantization

Quantization has been applied to multiple domains in Deep Neural Network...

Please sign up or login with your details

Forgot password? Click here to reset