Fine-Grained Video-Text Retrieval via Temporally Aligned Mutually Reinforced Cross-Modal Learning

Abstract

The increasing volume of multimedia content has made video text retrieval of significant practical importance. However, the fundamental challenge of matching the fine-grained temporal dynamics of videos with the sequence structures described in natural language remains largely unresolved. This paper investigates a framework that attempts to bridge this cross-modal semantic gap through the principled integration of two complementary representation strategies. Our preliminary experiments on standard benchmarks demonstrate that the proposed method achieves significant improvements over baseline configurations, particularly for action categories with complex temporal structures. However, the performance gains are unevenly distributed across different text structures and are highly sensitive to the choice of visual backbone. This study rationally integrates existing cross-modal retrieval strategies and, supplemented by empirical evidence, suggests that meaningful progress may rely less on inventing entirely new modules and more on a thoughtful coordination of temporal alignment principles.