Weakly Supervised Deep Hyperspherical Quantization for Image Retrieval
Jinpeng Wang Bin Chen Qiang Zhang Zaiqiao Meng Shangsong Liang Shu-Tao Xia
Deep quantization methods have shown high efficiency on large-scale image
retrieval. However, current models heavily rely on ground-truth information,
hindering the application of quantization in label-hungry scenarios. A more
realistic demand is to learn from inexhaustible uploaded images that are
associated with informal tags provided by amateur users. Though such sketchy
tags do not obviously reveal the labels, they actually contain useful semantic
information for supervising deep quantization. To this end, we propose
Weakly-Supervised Deep Hyperspherical Quantization (WSDHQ), which is the first
work to learn deep quantization from weakly tagged images. Specifically, 1) we
use word embeddings to represent the tags and enhance their semantic
information based on a tag correlation graph. 2) To better preserve semantic
information in quantization codes and reduce quantization error, we jointly
learn semantics-preserving embeddings and supervised quantizer on hypersphere
by employing a well-designed fusion layer and tailor-made loss functions.
Extensive experiments show that WSDHQ can achieve state-of-art performance on
weakly-supervised compact coding. Code is available at
https://github.com/gimpong/AAAI21-WSDHQ.