Exploring Hierarchical Cross-Modal Correlation Consistency for Partial Mismatching.

Area of Science:

• Computer Science
• Artificial Intelligence
• Information Retrieval

Background:

• Cross-modal retrieval enhances information access and semantic understanding across diverse data types.
• Traditional models require perfectly aligned datasets, which are costly and difficult to acquire.
• Real-world data often contains mismatched pairs, degrading retrieval performance.

Purpose of the Study:

• To develop a robust cross-modal retrieval method that addresses challenges posed by partially mismatched data.
• To improve semantic matching and inter-class separability in the presence of data inconsistencies.

Main Methods:

• Proposed Exploring Hierarchical Cross-Modal Correlation Consistency (EH3C) for cross-modal retrieval.
• Leveraged neighborhood correlation distributions for cross-modal alignment without assuming ideal distributions.
• Employed intra-modal correlation learning using negative sample pairs to enhance inter-class separability.

Main Results:

• EH3C effectively measures soft matching degrees and learns positive correlations between cross-modal data.
• The method enhances inter-class separability by exploiting negative correlations.
• Extensive experiments on benchmark datasets validated the significant performance improvement of EH3C.

Conclusions:

• EH3C offers a robust solution for cross-modal retrieval in scenarios with partial data mismatches.
• The approach improves semantic understanding and retrieval accuracy compared to traditional methods.
• EH3C demonstrates effectiveness and robustness in handling real-world, imperfect datasets.