KALAHash：面向深度哈希的知识锚定低资源适配
KALAHash: Knowledge-Anchored Low-Resource Adaptation for Deep Hashing
AI总结：
针对深度哈希在极少训练样本下的低资源适配问题，本文提出KALAHash，通过类校准LoRA和知识引导离散优化利用类级知识锚定微调与哈希码优化，显著提升检索性能并实现4倍数据效率。
摘要：
深度哈希因其存储和搜索效率高而被广泛用于大规模近似最近邻搜索。然而，现有深度哈希方法主要依赖充足的训练数据，使得更具挑战性的深度哈希低资源适配场景相对未被充分探索。该场景涉及仅用极少量训练样本将预训练模型适配到下游任务。我们的初步基准测试表明，由于有限训练样本引起的分布偏移，当前方法性能显著下降。为应对这些挑战，我们提出类校准LoRA（CLoRA），这是一种新颖的即插即用方法，通过利用类级文本知识嵌入动态构建低秩适配矩阵。CLoRA有效引入先验类知识作为锚点，在保持原始数据分布的同时实现参数高效微调。此外，我们提出知识引导离散优化（KIDDO），这是一个利用类知识弥补视觉信息稀缺并增强哈希码判别力的框架。大量实验表明，我们提出的方法——知识锚定低资源适配哈希（KALAHash）——显著提升检索性能，并在低资源场景下实现4倍数据效率。
英文摘要：
Deep hashing has been widely used for large-scale approximate nearest neighbor search due to its storage and search efficiency. However, existing deep hashing methods predominantly rely on abundant training data, leaving the more challenging scenario of low-resource adaptation for deep hashing relatively underexplored. This setting involves adapting pre-trained models to downstream tasks with only an extremely small number of training samples available. Our preliminary benchmarks reveal that current methods suffer significant performance degradation due to the distribution shift caused by limited training samples. To address these challenges, we introduce Class-Calibration LoRA (CLoRA), a novel plug-and-play approach that dynamically constructs low-rank adaptation matrices by leveraging class-level textual knowledge embeddings. CLoRA effectively incorporates prior class knowledge as anchors, enabling parameter-efficient fine-tuning while maintaining the original data distribution. Furthermore, we propose Knowledge-Guided Discrete Optimization (KIDDO), a framework to utilize class knowledge to compensate for the scarcity of visual information and enhance the discriminability of hash codes. Extensive experiments demonstrate that our proposed method, Knowledge- Anchored Low-Resource Adaptation Hashing (KALAHash), significantly boosts retrieval performance and achieves a 4x data efficiency in low-resource scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
