  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-prototype-mixed
contrastive loss and a semantic-enhanced loss, to counteract category space
differences. In the second stage, through a modified adversarial training
mechanism, we ensure minimal changes for the established prototypical structure
during domain alignment, enabling more accurate nearest-neighbor searching.
Extensive experiments across multiple datasets and scenarios, including closet,
partial, and open-set CDR, demonstrate that our approach significantly
outperforms existing state-of-the-art CDR works and some potentially effective
studies from other topics in solving U^2CDR challenges.
