Rebalanced Vision-Language Retrieval Considering Structure-Aware Distillation.

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Vision-language retrieval seeks to align representations from different modalities in a shared latent space.
• Modal balance, where each modality sufficiently represents the others, is a key assumption.
• Modal imbalance, caused by noise or insufficient information, is a common challenge impacting retrieval performance.

Purpose of the Study:

• To investigate the impact of modal imbalance on cross-modal retrieval.
• To propose a novel method for rebalancing modalities and improving retrieval accuracy.
• To enhance both cross-modal and single-modal retrieval capabilities.

Main Methods:

• Demonstrated that standard cross-modal matching is suboptimal under modal imbalance.
• Introduced structure-preserved matching to address challenges in similarity measurement.
• Developed a multi-granularity cross-modal matching approach with structure-aware distillation and relational matching.

Main Results:

• The proposed method effectively rebalances cross-modal matching by learning structure-preserved representations.
• Structure-aware distillation regularizes geometric consistency between cross-modal and intra-modal representations.
• Experimental results show superior cross-modal retrieval performance and improved single-modal retrieval.

Conclusions:

• Modal imbalance significantly affects cross-modal retrieval, necessitating specialized approaches.
• Structure-preserved matching offers a robust solution for rebalancing modalities.
• The proposed method achieves state-of-the-art performance, highlighting the importance of structural consistency in cross-modal learning.