Deep Stochastic Spherical Hashing With Von Mises-Fisher Distributions for Cross-Modal Retrieval

Deep cross-modal hashing has gained significant attention because of its benefits, including reduced storage requirements and enhanced retrieval efficiency. Although progress has been made, existing deep cross-modal hashing methods still face unresolved challenges. Most existing methods typically adopt Euclidean space as the embedding space to measure the semantic similarity between original samples. However, the volume of Euclidean space grows polynomially with dimension, which exacerbates the curse of dimensionality. In contrast, methods based on spherical space usually use cosine similarity as the metric, effectively mitigating the aforementioned problem by normalizing the embedding vectors. Nevertheless, such methods only considers the direction to determine the category, ignoring the uncertainty measure in the embedding space, thus having a limited ability to preserve inherent multimodal semantics. In this paper, with a novel extension of the maximum entropy distribution on the surface of a hypersphere von Mises-Fisher (vMF) distribution, a novel deep cross-modal hashing method, named Deep Stochastic Spherical Hashing (DSSH), is designed to utilize uncertain information to guide the hashing process and produce discriminative modality-invariant hash codes. Specifically, to learn explicit uncertainty in learned embedding space, the Spherical von Mises-Fisher distribution is applied for the f irst time in deep cross-modal hashing, where the direction of the sample embedding controls its position on the hyper sphere, thereby preventing its semantic content, and its norm parameterizes the determinism of the distribution. In addition, stochastic spherical von Mises–Fisher loss is proposed to preserve the mode-specific semantic information of the sample, achieving the alignment of different modalities and semantic embeddings. Extensive experiments on four benchmark datasets show that our DSSH framework outperforms existing state-of-the-art cross modal hashing methods. The source code of the experiments is available at https://github.com/QinLab-WFU/DSSH.