In this paper, we investigate the problem of unsupervised domain adaptive hashing, which
leverage knowledge from a label-rich source domain to expedite learning to hash on a label …

This paper studies the problem of semi-supervised 2D-3D retrieval which aims to align both
labeled and unlabeled 2D and 3D data into the same embedding space. The problem is …

Supervised fine-tuning (SFT) is crucial in adapting large language models (LLMs) to a
specific domain or task. However, only a limited amount of labeled data is available in …

Unsupervised domain adaptive hashing is a highly promising research direction within the
field of retrieval. It aims to transfer valuable insights from the source domain to the target …

In our increasingly interconnected world, where intelligent devices continually amass
copious personalized multi-modal data, a pressing need arises to deliver high-quality …

Domain adaptive hashing has received increasing attention since it is capable of enhancing
the performance of retrieval if the target domain for testing meets domain shift. However …