analiza aiqbench
This paper addresses critical bottlenecks in domain adaptive retrieval by introducing a two-stage framework that prioritizes class-level semantic alignment and geometric guidance over simple pairwise sample matching. By reconstructing features before quantization, the proposed method mitigates quality degradation caused by direct feature compression under domain shift, resulting in more robust binary hash codes across different environments.
Prototype-Based Semantic Consistency Alignment for Domain Adaptive Retrieval
arXiv:2512.04524v4 Announce Type: replace Abstract: 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.
Abstract: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.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2512.04524 [cs.LG]
(or arXiv:2512.04524v4 [cs.LG] for this version)
Text integral sub licența deschisă a publicației · 205 cuvinte
Subiecte conexe
How Cohere Health digitizes clinical policies using Amazon Bedrock AgentCore
AWS · 7 aug. 2026
Presentation: Building Evals for AI Adoption: from Principles to Practice
InfoQ · 29 mai 2026
Scaling medical content review at Flo Health with Amazon Bedrock – Part 2
AWS · 14 iul. 2026
NeST: Neighborhood-aware semantic alignment and temporal modulation for LLM based time series forecasting
arXiv · 25 aug. 2026