Unsupervised cross-modal hashing retrieval via Dynamic Contrast and Optimization

Cross-modal hashing encodes multimodal data into a common binary space, which can efficiently measure correlations between cross-modal instances. However, most existing cross-modal hashing retrieval methods are difficult to handle the heterogeneity problem between different modalities, and the performance drops because the binary code cannot be learned in the process of hash binary optimization. To solve these problems, we propose a Dynamic Contrast and Optimization (DCO) method for unsupervised cross-modal hashing retrieval, which implements an adaptive hash optimizer to strengthen the consistency of each modal representation and maintain the correlations between different modalities. Specifically, we propose a novel adaptive memory optimization mechanism. It enables the memory unit to learn and optimize adaptively, memorize in dynamic learning, and learn from memory, thereby narrowing the gap between original features and binary representations. Furthermore, we combine cross-modal ranking learning and adversarial learning. This not only ensures the modal invariance of correlated binary codes, but also allows for better approximation of generating continuous values close to discrete binary codes. To verify the effectiveness of the proposed method, we conduct a series of experiments on three widely used benchmark datasets. Through experimental results, we demonstrate the superiority of the proposed method in comparison with some state-of-the-art methods.