Discriminative Quantization for Fast Similarity Search
Abstract
Recent decade has witnessed a growing surge of research on encoding high-dimensional objects with compact discrete codes. In this paper, we present a new supervised quantization technique to learn discriminative and compact codes for large scale retrieval tasks. To achieve fast and accurate search, the proposed algorithm learns a discriminative embedding of the input points and at the same time encodes the embedded points with compact codes to reduce storage cost.
Cite
Text
Eghbali and Tahvildari. "Discriminative Quantization for Fast Similarity Search." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.Markdown
[Eghbali and Tahvildari. "Discriminative Quantization for Fast Similarity Search." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.](https://mlanthology.org/cvprw/2019/eghbali2019cvprw-discriminative/)BibTeX
@inproceedings{eghbali2019cvprw-discriminative,
title = {{Discriminative Quantization for Fast Similarity Search}},
author = {Eghbali, Sepehr and Tahvildari, Ladan},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2019},
url = {https://mlanthology.org/cvprw/2019/eghbali2019cvprw-discriminative/}
}