Semantic-Decoupled and Knowledge-Shared Probabilistic Mapping Network for Multi-Grained Cross-Modal Retrieval

Cross-modal retrieval is essential for exploring semantic correlations between multimodal data. However, existing approaches face challenges in resolving semantic ambiguity and transferring knowledge with sparse sample generalization. To address these challenges, we propose a new Semantic-Decoupled and Knowledge-Shared Probabilistic Mapping Network (SKPMN). Specifically, the Semantic Decoupling and Distinction (SDD) module decomposes complex word-region relationships into relevance-driven representations. The Deep Probability Mapping (DPM) module introduces a paradigm shift by mapping multimodal features into probabilistic distributions, capturing the semantic similarities and the potential uncertainties that define sparse or ambiguous relationships. By combining the Attention Probabilistic Mapping (APM) module, the model can effectively transfer knowledge across similar samples while emphasizing critical distinctions, significantly enhancing generalization to sparse and ambiguous samples. Finally, the multi-grained alignment strategy establishes a novel integration of fine-grained patch-to-word alignment and coarse-grained global alignment. Experimental results show that SKPMN achieves superior retrieval accuracy across major benchmark datasets. Furthermore, we implement a channel resource allocation technique that allocates more transmission resources to semantically significant information. In resource-constrained environments, our approach leverages Joint Source-Channel Coding (JSCC) to enhance the efficiency of visual feature transmission.