Global-Local Contrastive Consistency Learning for Cross-Domain Video-Text Retrieval Accuracy

Abstract

Abstract: Adapting large-scale image-text pre-training models, e.g., CLIP, to the video domain represents the current state-of-the-art for text-video retrieval. The primary approaches involve transferring text-video pairs to a common embedding space and leveraging cross-modal interactions on specific entities for semantic alignment. Though effective, these paradigms entail prohibitive computational costs, leading to inefficient retrieval. To address this, we propose a simple yet effective method, Global-Local Semantic Consistent Learning (GLSCL), which capitalizes on latent shared semantics across modal

Research Question

What is the impact of global-local contrastive consistency learning on retrieval accuracy when applied to cross-domain video-text datasets such as MSR-VTT and ActivityNet compared to attention-based models?

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