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标注源域的知识迁移至未标注目标域，在缓解域间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）忽视类别级语义对齐而过度追求成对样本对齐；2）缺乏对伪标签可靠性的考量或评估标签正确性的几何引导；3）直接量化受域偏移影响的原始特征，损害学习哈希码的质量。针对上述局限，我们提出基于原型的语义一致性对齐（PSCA）——一种用于高效域自适应检索的两阶段框架。第一阶段通过一组正交原型直接建立类别级语义连接，在聚集类内样本的同时最大化类间可分性。原型学习过程中，几何邻近性通过自适应加权伪标签置信度，为语义一致性对齐提供可靠性指标。由之生成的隶属度矩阵与原型促进特征重建，确保对重建特征而非原始特征进行量化，进而提升后续哈希编码质量并实现两阶段无缝衔接。第二阶段采用领域特定量化函数在互逼近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。