多长度跨模态哈希检索的尺度可分解分数阶扩散相关性分辨率迁移
Relevance-Resolution Transfer via Scale-Decomposable Fractional Diffusion for Multi-Length Cross-Modal Hash Retrieval
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中文总结 AI 辅助
提出MultiBit方法，通过尺度可分解分数阶扩散将多标签相关性分辨率迁移至多长度汉明空间，解决相关性分辨率瓶颈，提升跨模态哈希检索精度。
中文摘要 AI 辅助
跨模态哈希通过将异构数据编码为紧凑的二进制码实现高效检索。近期方法利用多标签训练结构中编码的细粒度关系，但均未约束这些关系在有限、多长度的汉明空间中如何作为一致候选排名存续，我们称之为相关性分辨率瓶颈（RRB）。为解决RRB，我们提出MultiBit，将相关性分辨率从多标签结构迁移至多长度汉明空间。MultiBit首先从数据集级标签共现和标签特异性构建尺度可分解的分数阶关系教师，并在连续扩散尺度上建模从局部到长程的依赖关系。随后，它将离散化的扩散尺度及其求积权重映射到最大长度码的尺度感知位子块，将目标码长组织为嵌套前缀，并使其汉明候选排名与教师关系对齐。在多个基准上的实验表明检索精度提升。代码见补充材料。
英文摘要
Cross-modal hashing enables efficient retrieval by encoding heterogeneous data into compact binary codes. Recent methods exploit fine-grained relations encoded in multi-label training structure, yet none of them constrains how those relations survive as consistent candidate rankings in finite, multi-length Hamming spaces, which we term the relevance resolution bottleneck (RRB). To address the RRB, we propose MultiBit, which transfers relevance resolution from multi-label structure to multi-length Hamming spaces. MultiBit first constructs a scale-decomposable fractional relation teacher from dataset-level label co-occurrence and label specificity, and models dependencies from local to long-range over continuous diffusion scales. It then maps the discretized diffusion scales and their quadrature weights to scale-aware bit subblocks of the maximum-length code, organizes the target code lengths as nested prefixes, and aligns their Hamming candidate rankings with the teacher relations. Experiments on multiple benchmarks demonstrate improved retrieval accuracy. Code is available in the supplementary material.
发表机构
- School of Computer Science, Wuhan University（武汉大学计算机学院）
机构由 AI 辅助整理，请以论文原文为准。
