Remote sensing image–text retrieval (RSITR) requires matching not only scene semantics but also fine-grained spatial layouts. Existing cross-modal retrieval methods commonly align global image and sentence representations, which may confuse queries containing similar object vocabularies but different spatial relations....
Xi Chen, Han Yu, Shan-Hua Wang et al.· IEEE Geoscience and Remote S...· 0 citations
Remote sensing (RS) image-text retrieval plays a critical role in understanding massive RS imagery. However, the dense multi-object distribution and complex backgrounds in RS imagery make it difficult to simultaneously achieve fine-grained cross-modal alignment and efficient retrieval. Existing methods either rely on c...
Xi Chen, Xu Chen, Xiamgyang Jia et al.· 0 citations
Cross-modal hashing enables efficient retrieval by encoding heterogeneous data into compact binary codes. Recent methods exploit fine-grained relations encoded in multi-label training structure, yet none of them constrains how those relations survive as consistent candidate rankings in finite, multi-length Hamming spac...
Xi Chen, Xu Chen, Xiang-Yang Jia et al.· 1 citation
Text-video retrieval, which aims to bridge visual and textual modalities by learning a joint embedding space, has become a crucial task in multimodal intelligence. Despite extensive efforts to mitigate visual redundancy, previous methods typically rely on a single-aspect criterion to assess visual importance, overlooki...
Shu-Quan Wei, Xi Chen, Xu Chen et al.· arXiv.org· 0 citations
DARAD is proposed, a dual-adapter and ranking-aware distillation framework that preserves the historical cross-modal ranking structure while learning new visual and textual concepts from evolving archives to address the challenge of continual RS-ITR.
Xi Chen, Xu Chen, Xiang-Yang Jia et al.· 0 citations
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