Optimizing Multimodal Feature Alignment for Enhanced Text-to-Video Retrieval Performance

Multimodal AI integrates text, images, video, and audio to create models with a more comprehensive understanding of real-world interactions. A fundamental challenge in multimodal learning is ensuring proper feature alignment between modalities, which significantly impacts retrieval accuracy and generative quality. In this paper, we focus on optimizing multimodal feature alignment for improved text-to-video retrieval performance. We propose an efficient contrastive learning approach to align text, video, and audio embeddings, leveraging pretrained models (e.g., CLIP, Flamingo) with lightweight finetuning techniques (LoRA, frozen encoders). Experimental evaluations demonstrate a 32% improvement in Recall@5, highlighting the importance of structured feature alignment. Our findings provide a computationally feasible strategy for advancing multimodal retrieval models. Furthermore, we address bias mitigation as an essential component of robust multimodal AI, illustrating how dataset reweighting and targeted correction techniques can foster more balanced generative outputs.