V-Sparse: From temporal-spatial visual semantic compression to coarse-to-fine interaction for text-video retrieval.

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Text-to-video retrieval aims to find relevant videos using text queries from large unlabeled collections.
• CLIP-based methods focus on feature enhancement and interaction but struggle with cross-modal matching imbalance.
• Current methods are insufficient for high-precision retrieval due to modality differences.

Purpose of the Study:

• To propose a novel text-video retrieval model, V-Sparse, addressing cross-modal matching imbalance.
• To introduce visual semantic compression (VSC) for feature enhancement and coarse-to-fine alignment (CFI) for feature interaction.
• To improve the precision and effectiveness of text-to-video retrieval.

Main Methods:

• Developed a text-guided Visual Semantic Compression (VSC) module with Temporal (TVSC) and Spatial (SVSC) components to reduce feature redundancy.
• Introduced a Coarse-to-Fine granularity Interaction (CFI) module for aligning sentences with frames, sentences with patches, and words with patches.
• Employed a unified joint feature encoding perspective for VSC and CFI to facilitate cross-modal alignment.

Main Results:

• V-Sparse achieved state-of-the-art results on six benchmark datasets for both long-video and short-text retrieval.
• Demonstrated the effectiveness of feature compression in cross-modal interaction through extensive ablation studies.
• Showcased V-Sparse as an effective intermediate pathway for modality interaction.

Conclusions:

• V-Sparse effectively mitigates the inherent imbalance in text-video modal pairing.
• The proposed VSC and CFI modules significantly enhance cross-modal text-video alignment.
• V-Sparse offers a promising direction for advancing text-to-video retrieval technologies.