Comparative Performance of the MASS Framework on ActivityNet Captions and Charades-STA Benchmarks

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

How does the performance of the MASS framework on the ActivityNet Captions and Charades-STA benchmarks compare to other state-of-the-art text-video retrieval models (e.g., CLIP, UNITER) in terms of mAP@10 and recall@K metrics?

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