Hyperbolic Hierarchical Contrastive Hashing

TL;DR

This paper introduces Hyperbolic Hierarchical Contrastive Hashing (HHCH), an unsupervised method that embeds hierarchical semantic structures into hyperbolic space to improve data retrieval accuracy.

Contribution

The paper proposes a novel hyperbolic embedding and hierarchical contrastive learning approach for unsupervised hashing, effectively capturing hierarchical semantics.

Findings

Outperforms state-of-the-art unsupervised hashing methods on four benchmarks.

Effectively embeds hierarchical structures with less distortion in hyperbolic space.

Demonstrates the superiority of hyperbolic space for semantic hierarchy modeling.

Abstract

Hierarchical semantic structures, naturally existing in real-world datasets, can assist in capturing the latent distribution of data to learn robust hash codes for retrieval systems. Although hierarchical semantic structures can be simply expressed by integrating semantically relevant data into a high-level taxon with coarser-grained semantics, the construction, embedding, and exploitation of the structures remain tricky for unsupervised hash learning. To tackle these problems, we propose a novel unsupervised hashing method named Hyperbolic Hierarchical Contrastive Hashing (HHCH). We propose to embed continuous hash codes into hyperbolic space for accurate semantic expression since embedding hierarchies in hyperbolic space generates less distortion than in hyper-sphere space and Euclidean space. In addition, we extend the K-Means algorithm to hyperbolic space and perform the proposed hierarchical hyperbolic K-Means algorithm to construct hierarchical semantic structures adaptively. To exploit the hierarchical semantic structures in hyperbolic space, we designed the hierarchical contrastive learning algorithm, including hierarchical instance-wise and hierarchical prototype-wise contrastive learning. Extensive experiments on four benchmark datasets demonstrate that the proposed method outperforms the state-of-the-art unsupervised hashing methods. Codes will be released.