Coupled CycleGAN: Unsupervised Hashing Network for Cross-Modal Retrieval
This work addresses the need for efficient and accurate cross-modal retrieval in large-scale applications, offering an unsupervised solution that is incremental over existing methods.
The paper tackles the problem of unsupervised cross-modal retrieval by proposing a coupled CycleGAN-based hashing network (UCH) that learns common representations and generates hash codes without labeled data, achieving state-of-the-art performance on three benchmark datasets.
In recent years, hashing has attracted more and more attention owing to its superior capacity of low storage cost and high query efficiency in large-scale cross-modal retrieval. Benefiting from deep leaning, continuously compelling results in cross-modal retrieval community have been achieved. However, existing deep cross-modal hashing methods either rely on amounts of labeled information or have no ability to learn an accuracy correlation between different modalities. In this paper, we proposed Unsupervised coupled Cycle generative adversarial Hashing networks (UCH), for cross-modal retrieval, where outer-cycle network is used to learn powerful common representation, and inner-cycle network is explained to generate reliable hash codes. Specifically, our proposed UCH seamlessly couples these two networks with generative adversarial mechanism, which can be optimized simultaneously to learn representation and hash codes. Extensive experiments on three popular benchmark datasets show that the proposed UCH outperforms the state-of-the-art unsupervised cross-modal hashing methods.