Semantic Feature Learning For Universal Unsupervised Cross-domain Retrieval

Abstract

Cross-domain retrieval (CDR), as a crucial tool for numerous technologies, is finding increasingly broad applications. However, existing efforts face several major issues, with the most critical being the need for accurate supervision, which often demands costly resources and efforts. Cutting-edge studies focus on achieving unsupervised CDR but typically assume that the category spaces across domains are identical, an assumption that is often unrealistic in real-world scenarios. This is because only through dedicated and comprehensive analysis can the category spaces of different domains be confirmed as identical, which contradicts the premise of unsupervised scenarios. Therefore, in this work, we introduce the problem of Universal Unsupervised Cross-Domain Retrieval (U^2CDR) for the first time and design a two-stage semantic feature learning framework to address it. In the first stage, a cross-domain unified prototypical structure is established under the guidance of an instance-proto

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keywang2024semantic

Related papers

• Feature Representation Learning For Unsupervised Cross-domain Image Retrieval (2022)11.46
• Test-time Training For Data-efficient UCDR (2022)0.00
• Text-phase Synergy Network With Dual Priors For Unsupervised Cross-domain Image Retrieval (2026)0.00
• Universal Cross-domain Retrieval: Generalizing Across Classes And Domains (2021)8.09
• Discriminative Supervised Subspace Learning For Cross-modal Retrieval (2022)0.00
• DUDE: Diffusion-based Unsupervised Cross-domain Image Retrieval (2025)0.00
• Unsupervised Cross-domain Image Retrieval Via Prototypical Optimal Transport (2024)8.09
• Efficient And Discriminative Image Feature Extraction For Universal Image Retrieval (2024)4.94