Collision-Resistant Single-Pass Method for Unsupervised Fine-Grained Image Hashing

2026-05-18

AI Summary

This work addresses the challenge in unsupervised fine-grained image hashing where neglecting collision resistance often causes semantically similar yet distinct samples to be mapped to identical hash codes. To this end, the paper proposes the CS3H framework, which explicitly models collision resistance for the first time in this task. Specifically, it introduces a normalized Hamming distance loss computed in a single forward pass to directly optimize similarity in Hamming space, and incorporates a collision-aware attention module to enhance the learning of rare yet discriminative local features. Extensive experiments demonstrate that CS3H significantly outperforms existing methods across multiple benchmark datasets, achieving higher retrieval accuracy and stronger collision resistance while maintaining low computational overhead.

Abstract

Unsupervised fine-grained image hashing aims to learn compact binary codes that preserve subtle visual differences among highly similar instances without manual annotations. However, most existing methods neglect collision resistance, leading to identical hash codes for slightly semantically different samples. In this paper, we propose Collision-Resistant Single-Pass Self-Supervised Semantic Hashing (CS3H), a collision-resistant framework that directly optimizes Hamming-space similarity via a single-pass normalized Hamming distance loss to produce well-separated binary representations. We further introduce a collision-sensitive attention module to emphasize rare and discriminative local patterns, reducing hash collisions and improving fine-grained discrimination. Experiments on multiple benchmarks show that CS3H consistently outperforms state-of-the-art methods in retrieval accuracy while achieving superior collision resistance with minimal computational overhead.

Problem

• unsupervised fine-grained image hashing
• collision resistance
• hash collisions
• binary codes
• semantic discrimination

Innovation

• collision-resistant hashing
• single-pass optimization
• fine-grained image retrieval
• self-supervised hashing
• attention mechanism