ViC-MAE Contrastive Alignment for Zero-Shot Video-Text Retrieval Accuracy on MSR-VTT

Abstract

Abstract: We present a simple yet effective end-to-end Video-language Pre-training (VidLP) framework, Masked Contrastive Video-language Pretraining (MAC), for video-text retrieval tasks. Our MAC aims to reduce video representation's spatial and temporal redundancy in the VidLP model by a mask sampling mechanism to improve pre-training efficiency. Comparing conventional temporal sparse sampling, we propose to randomly mask a high ratio of spatial regions and only feed visible regions into the encoder as sparse spatial sampling. Similarly, we adopt the mask sampling technique for text inputs for consisten

Research Question

How does ViC-MAE's contrastive alignment mechanism impact zero-shot video-text retrieval accuracy on the MSR-VTT benchmark compared to standard MAE pre-training?

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