吸收梯度冲突：通过 Kent 分布建模语义方差用于跨模态哈希

中文摘要 AI 辅助

基于代理的监督式深度跨模态哈希已成为大规模检索的主流范式。然而，现有主流方法将类代理建模为嵌入空间中的确定性点，这种刚性假设在多标签场景中会引发严重的梯度冲突——标签共现导致的梯度冲突会引发剧烈的梯度竞争与优化崩溃。为解决该问题，我们提出了基于 Kent 分布的分布式代理哈希框架（Kent-based Distributional Proxy Hashing，KDPH），该框架将代理表示从静态点转换为超球面上灵活的各向异性 Kent 分布。与必须移动位置以适配冲突梯度的点代理不同，KDPH 通过动态调整方向方差吸收这些冲突，使代理在保持稳定语义均值方向的同时，覆盖多样的标签相关性。此外，为确保这些几何参数的训练稳定性，我们推导了结合 Cayley 变换的定制损失函数，以强制执行严格正交性。据我们所知，KDPH 是首个成功将 Kent 分布引入跨模态哈希的框架。在三个基准数据集上的实验表明，KDPH 缓解了代理崩溃与混沌振荡，显著优于现有最优方法。代码可在指定 URL 获取。

英文摘要

Supervised proxy-based deep cross-modal hashing has become the dominant paradigm for large-scale retrieval. However, prevalent methods model class proxies as deterministic points in the embedding space. This rigid assumption causes severe gradient conflicts in multi-label scenarios, where gradient conflicts arising from label co-occurrence lead to severe gradient contention and optimization collapse. To resolve this, we propose Kent-based Distributional Proxy Hashing (KDPH), a novel framework that shifts proxy representation from static points to flexible anisotropic Kent distributions on the hypersphere. Unlike point proxies that must shift their positions to accommodate conflicting gradients, KDPH absorbs these conflicts by dynamically adjusting its directional variance. This allows the proxy to maintain a stable semantic mean direction while stretching to cover diverse label correlations. Furthermore, to ensure stable training of these geometric parameters, we derive a tailored loss function incorporating the Cayley transform to enforce strict orthogonality. To the best of our knowledge, KDPH is the first framework to successfully introduce the Kent distributions into cross-modal hashing. Experiments on three benchmark datasets demonstrate that KDPH mitigates proxy collapse and chaotic oscillation, significantly outperforms state-of-the-art methods. Code is available at https://github.com/Senmo996/KDPH-official-code.

发表机构

• Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）
• School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）
• School of Cyber Science and Engineering, Southeast University（东南大学网络空间安全学院）

机构由 AI 辅助整理，请以论文原文为准。