Research map / Vision-language and multimodal / Cross-modal learning
UGGRH: Unsupervised Generative Completion and Graph-attention Refinement for Incomplete Cross-modal Hashing
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Proposes UGGRH, a cross-modal hashing method for incomplete multimodal data that combines unsupervised generative completion with graph-attention refinement.
Problem
Cross-modal hashing methods mostly assume complete paired multimodal data, which rarely holds in practice; the paper targets retrieval when modalities are missing.
Approach
Unsupervised generative completion of missing modalities followed by graph-attention refinement of the hash codes, as suggested by the title; details beyond the title are not available in the provided abstract.
Closest papers by meaning
- Adaptive Graph Attention Based Discrete Hashing for Incomplete Cross-modal Retrieval (AAAI 2026 · similarity 0.92)
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- Statistical Model-driven Similarity Hashing: Bridging Modalities for Efficient Unsupervised Retrieval (AAAI 2025 · similarity 0.89)
- Exploiting Descriptive Completeness Prior for Cross Modal Hashing with Incomplete Labels (NeurIPS 2024 · similarity 0.89)
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