Data-Focus Proxy Hashing

Cross-modal hashing approaches are designed to transform data from disparate modalities into a shared Hamming space while preserving the semantic similarity relationships between the modalities. Generally, most supervised cross-modal hashing methods treat the entire data as input. However, the total information within the data encompasses both vital semantic details and extraneous background noise. Such background information may introduce noise and undermine the preservation of semantic similarity relationships between modalities. To address this issue, we propose a novel Data-Focus Proxy Hashing (DFPH) for cross-modal retrieval, which emphasizes important data information. Our approach begins by training an intermediary network designed to produce a unique proxy hash code specific to each individual category. Then, by incorporating attention mechanisms, and vital textual information, we first design a novel modality-specific hashing network, including an image-focused hash codes generator and a text-focused hash codes generator. Subsequently, we utilize the hashing loss function to train the hashing network under the supervision of these proxy hash codes. Extensive experiments on two benchmarks demonstrate that our proposed DFPH outperforms existing baselines in cross-modal retrieval tasks. Our code is available at https://github.com/JWJ990626/DFPH.