arXiv 2512.045242026-06-17cs.LGcs.AI

基于原型语义一致性对齐的域自适应检索

Prototype-Based Semantic Consistency Alignment for Domain Adaptive Retrieval

School of Computer Science and Technology, Guangdong University of Technology（广东工业大学计算机科学与技术学院）；School of Automation, Guangdong University of Technology（广东工业大学自动化学院）；School of Computer Science, Guangdong Polytechnic Normal University（广东 polytechnic 正规大学计算机科学学院）；School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen（哈尔滨工业大学深圳校区计算机科学与技术学院）；School of Artificial Intelligence, Guangzhou University（广州大学人工智能学院）

Tianle Hu, Weijun Lv, Na Han, Xiaozhao Fang, Jie Wen, Jiaxing Li, Guoxu Zhou

备注：: AAAI2026

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AI总结：

提出原型语义一致性对齐（PSCA）两阶段框架，通过正交原型建立类级语义连接，利用几何邻近性加权伪标签置信度，并在重构特征上量化生成统一哈希码，解决域自适应检索中的类级对齐缺失和量化质量下降问题。

摘要：

域自适应检索旨在将知识从有标签的源域迁移到无标签的目标域，实现有效检索的同时缓解域差异。然而，现有方法存在几个根本性局限：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虑或评估标签正确性的几何指导；3）直接量化受域偏移影响的原始特征，损害所学哈希码的质量。鉴于这些局限，我们提出基于原型的语义一致性对齐（PSCA），一种用于有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分离性。在原型学习过程中，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。所得的隶属度矩阵和原型促进特征重建，确保在重建特征而非原始特征上进行量化，从而改善后续哈希编码质量并无缝连接两个阶段。在第二阶段，特定域的量化函数在相互逼近约束下处理重建特征，生成跨域的统一二进制哈希码。大量实验验证了PSCA在多个数据集上的优越性能。

英文摘要：

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.

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