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.

翻译：使用紧凑二进制嵌入进行信息检索（通常称为哈希）对于可扩展的快速搜索应用至关重要，然而最先进的哈希方法需要昂贵且针对特定场景的训练。本工作提出哈希基线——一种利用强大预训练编码器生成丰富预训练嵌入的无训练哈希方法。我们重新审视经典的无训练哈希技术：主成分分析、随机正交投影和阈值二值化，从而构建出强大的哈希基准方法。该方法将这些技术与最先进的视觉和音频编码器生成的冻结嵌入相结合，无需任何额外学习或微调即可获得具有竞争力的检索性能。为验证该方法的通用性和有效性，我们在标准图像检索基准以及新引入的音频哈希基准上进行了评估。