An Empirical Study of End-to-End Video-Language Transformers with Masked Visual Modeling
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Masked visual modeling (MVM) has been recently proven effective for visual pre-training. While similar reconstructive objectives on video inputs (e.g., masked frame modeling) have been explored in video-language (VidL) pre-training, the pre-extracted video features in previous studies cannot be refined through MVM during pre-training, and thus leading to unsatisfactory downstream performance. In this work, we systematically examine the potential of MVM in the context of VidL learning. Specifically, we base our study on a fully end-to-end VIdeO-LanguagE Transformer (VIOLET), which mitigates the disconnection between fixed video representations and MVM training. In total, eight different reconstructive targets of MVM are explored, from low-level pixel values and oriented gradients to high-level depth maps, optical flow, discrete visual tokens and latent visual features. We conduct comprehensive experiments and provide insights on the factors leading to effective MVM training. Empirically, we show VIOLET pre-trained with MVM objective achieves notable improvements on 13 VidL benchmarks, ranging from video question answering, video captioning, to text-to-video retrieval.
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