MDA-MAA: A Collaborative Augmentation Approach for Generalizing Cross-Domain Retrieval.

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Cross-domain generalization is critical for video-text retrieval models.
• Existing models struggle with overfitting and limited multimodal data augmentation.
• This limits performance in diverse and complex cross-domain scenarios.

Purpose of the Study:

• To propose an innovative collaborative augmentation approach, MDA-MAA, to enhance cross-domain generalization in video-text retrieval.
• To address the limitations of unimodal augmentation and improve semantic diversity of training data.
• To boost the practical applicability and retrieval accuracy of models across different domains.

Main Methods:

• Developed the Masked Attention Augmentation (MAA) module to reduce overfitting by predicting masked video frame features.
• Implemented the Multimodal Diffusion Augmentation (MDA) module using LLaMA for caption inference and diffusion models for joint learning.
• Leveraged multimodal correlations between video and text for semantically enriched data augmentation.

Main Results:

• The MDA-MAA approach significantly improved the performance of video-text cross-domain retrieval models.
• Enhanced model generalization capabilities were demonstrated through experiments.
• The method effectively increased the semantic diversity of augmented training data.

Conclusions:

• The proposed MDA-MAA collaborative augmentation method is effective in enhancing video-text cross-domain retrieval.
• This approach successfully addresses overfitting and unimodal augmentation limitations.
• MDA-MAA validates the importance of leveraging multimodal relationships for improved model generalization.