Mutual Learning for Hashing: Unlocking Strong Hash Functions from Weak Supervision

• URL: http://arxiv.org/abs/2510.07703v1
• Date: Thu, 09 Oct 2025 02:39:05 GMT
• Title: Mutual Learning for Hashing: Unlocking Strong Hash Functions from Weak Supervision
• Authors: Xiaoxu Ma, Runhao Li, Zhenyu Weng,
• Abstract summary: Mutual Learning for Hashing (MLH) is a weak-to-strong framework that enhances a center-based hashing branch by transferring knowledge from a weaker pairwise-based branch.<n> MLH consistently outperforms state-of-the-art hashing methods across multiple benchmark datasets.
• Score: 8.509518175943537
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep hashing has been widely adopted for large-scale image retrieval, with numerous strategies proposed to optimize hash function learning. Pairwise-based methods are effective in learning hash functions that preserve local similarity relationships, whereas center-based methods typically achieve superior performance by more effectively capturing global data distributions. However, the strength of center-based methods in modeling global structures often comes at the expense of underutilizing important local similarity information. To address this limitation, we propose Mutual Learning for Hashing (MLH), a novel weak-to-strong framework that enhances a center-based hashing branch by transferring knowledge from a weaker pairwise-based branch. MLH consists of two branches: a strong center-based branch and a weaker pairwise-based branch. Through an iterative mutual learning process, the center-based branch leverages local similarity cues learned by the pairwise-based branch. Furthermore, inspired by the mixture-of-experts paradigm, we introduce a novel mixture-of-hash-experts module that enables effective cross-branch interaction, further enhancing the performance of both branches. Extensive experiments demonstrate that MLH consistently outperforms state-of-the-art hashing methods across multiple benchmark datasets.

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