Hashing-Baseline: Rethinking Hashing in the Age of Pretrained Models

• URL: http://arxiv.org/abs/2509.14427v1
• Date: Wed, 17 Sep 2025 20:58:43 GMT
• Title: Hashing-Baseline: Rethinking Hashing in the Age of Pretrained Models
• Authors: Ilyass Moummad, Kawtar Zaher, Lukas Rauch, Alexis Joly,
• Abstract summary: We introduce Hashing-Baseline, a strong training-free hashing method leveraging powerful pretrained encoders that produce rich pretrained embeddings.<n>Our approach combines these techniques with frozen embeddings from state-of-the-art vision and audio encoders to yield competitive retrieval performance without any additional learning or fine-tuning.
• Score: 4.531902882476647
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Information retrieval with compact binary embeddings, also referred to as hashing, is crucial for scalable fast search applications, yet state-of-the-art hashing methods require expensive, scenario-specific training. In this work, we introduce Hashing-Baseline, a strong training-free hashing method leveraging powerful pretrained encoders that produce rich pretrained embeddings. We revisit classical, training-free hashing techniques: principal component analysis, random orthogonal projection, and threshold binarization, to produce a strong baseline for hashing. Our approach combines these techniques with frozen embeddings from state-of-the-art vision and audio encoders to yield competitive retrieval performance without any additional learning or fine-tuning. To demonstrate the generality and effectiveness of this approach, we evaluate it on standard image retrieval benchmarks as well as a newly introduced benchmark for audio hashing.

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