Domain adaptive retrieval aims to transfer knowledge from a labeled source domain to an unlabeled target domain, enabling effective retrieval while mitigating domain discrepancies. However, existing methods encounter several fundamental limitations: 1) neglecting class-level semantic alignment and excessively pursuing pair-wise sample alignment; 2) lacking either pseudo-label reliability consideration or geometric guidance for assessing label correctness; 3) directly quantizing original features affected by domain shift, undermining the quality of learned hash codes. In view of these limitations, we propose Prototype-Based Semantic Consistency Alignment (PSCA), a two-stage framework for effective domain adaptive retrieval. In the first stage, a set of orthogonal prototypes directly establishes class-level semantic connections, maximizing inter-class separability while gathering intra-class samples. During the prototype learning, geometric proximity provides a reliability indicator for semantic consistency alignment through adaptive weighting of pseudo-label confidences. The resulting membership matrix and prototypes facilitate feature reconstruction, ensuring quantization on reconstructed rather than original features, thereby improving subsequent hash coding quality and seamlessly connecting both stages. In the second stage, domain-specific quantization functions process the reconstructed features under mutual approximation constraints, generating unified binary hash codes across domains. Extensive experiments validate PSCA's superior performance across multiple datasets.

翻译：域自适应检索旨在将知识从有标签的源域迁移至无标签的目标域，在缓解域差异的同时实现高效检索。然而，现有方法存在若干根本性局限：1）忽略类级语义对齐，过度追求成对样本对齐；2）缺乏伪标签可靠性考量或用于评估标签正确性的几何引导；3）对受域偏移影响的原始特征进行直接量化，损害了所学哈希码的质量。针对这些局限，本文提出基于原型的语义一致性对齐（PSCA）方法——一种面向高效域自适应检索的两阶段框架。在第一阶段，一组正交原型直接建立类级语义连接，在聚集类内样本的同时最大化类间可分性。在原型学习过程中，几何邻近性通过伪标签置信度的自适应加权，为语义一致性对齐提供可靠性指标。由此产生的隶属度矩阵与原型有助于特征重建，确保对重建特征而非原始特征进行量化，从而提升后续哈希编码质量并无缝衔接两阶段。在第二阶段，域特定量化函数在互近似约束下处理重建特征，生成跨域统一的二进制哈希码。大量实验验证了PSCA在多个数据集上的优越性能。