Deep Probabilistic Binary Embedding via Learning Reliable Uncertainty for Cross-Modal Retrieval

The field of cross-modal retrieval aims to construct a shared representation space for samples from multiple modalities, typically within the vision and language domains. Deep hashing, with its high computational efficiency and low storage costs, has emerged as a central focus in this field and has garnered significant attention in recent research. However, current hash retrieval, concentrating on deterministic methods, struggles to effectively capture semantically ambiguous correspondences between cross-modal samples, where heterogeneous data have complex-semantic many-to-many relationships in the latent space. To address this limitation, we propose a novel Deep Probabilistic Binary Embedding (DPBE) framework, designed to generate discriminative, modality-invariant hash codes that facilitate accurate and reliable cross-modal retrieval. In contrast to contemporary probabilistic methods, we focus on optimizing hash networks to learn more accurate binary embeddings by using the learning mode of probabilistic embeddings. We introduce the first Bayesian encoder for hash learning, which employs Laplace Approximation to model a distribution over network weights. Extensive experimental results demonstrate that our approach not only outperforms deterministic methods in retrieval performance but also provides uncertainty estimates, enhancing the interpretability of the embeddings. The corresponding code is available at https://github.com/QinLab-WFU/DPBE.