UniHash：统一点对点和配对哈希范式
UniHash: Unifying Pointwise and Pairwise Hashing Paradigms
AI总结：
UniHash通过统一点对点和配对哈希范式，提升图像检索在已见过和未见过类别上的性能。
摘要：
在现代图像检索系统中，实现对已见过和未见过类别的有效检索至关重要。对已见过类别的检索确保了对已知类别的精确识别，而对未见过类别的检索则促进了对新类别的泛化，仅需有限的监督。然而，大多数现有的深度哈希方法局限于单一的训练范式，即点对点或配对范式，其中前者在已见过类别上表现优异，后者在未见过类别上泛化更好。为克服这一限制，我们提出了统一哈希（UniHash），一种双分支框架，通过统一两种范式的优点，实现对已见过和未见过类别的平衡检索性能。UniHash由两个互补的分支组成：一个基于中心的分支遵循点对点范式，另一个分支遵循配对范式。引入了一种新的哈希码学习方法，以实现分支间的双向知识转移，提高哈希码的判别能力和泛化能力。它采用互学习损失对哈希表示进行对齐，并引入Split-Merge Mixture of Hash Experts（SM-MoH）模块来增强跨分支的哈希表示交换。理论分析证实了UniHash的有效性，广泛实验在CIFAR-10、MSCOCO和ImageNet上表明，UniHash在已见过和未见过的图像检索场景中都一致实现了最先进的性能。
英文摘要：
Effective retrieval across both seen and unseen categories is crucial for modern image retrieval systems. Retrieval on seen categories ensures precise recognition of known classes, while retrieval on unseen categories promotes generalization to novel classes with limited supervision. However, most existing deep hashing methods are confined to a single training paradigm, either pointwise or pairwise, where the former excels on seen categories and the latter generalizes better to unseen ones. To overcome this limitation, we propose Unified Hashing (UniHash), a dual-branch framework that unifies the strengths of both paradigms to achieve balanced retrieval performance across seen and unseen categories. UniHash consists of two complementary branches: a center-based branch following the pointwise paradigm and a pairwise branch following the pairwise paradigm. A novel hash code learning method is introduced to enable bidirectional knowledge transfer between branches, improving hash code discriminability and generalization. It employs a mutual learning loss to align hash representations and introduces a Split-Merge Mixture of Hash Experts (SM-MoH) module to enhance cross-branch exchange of hash representations. Theoretical analysis substantiates the effectiveness of UniHash, and extensive experiments on CIFAR-10, MSCOCO, and ImageNet demonstrate that UniHash consistently achieves state-of-the-art performance in both seen and unseen image retrieval 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。
