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• W4384080288 abstract "Video-language pre-training has attracted considerable attention recently for its promising performance on various downstream tasks. Most existing methods utilize the modality-specific or modality-joint representation architectures for the cross-modality pre-training. Different from previous methods, this paper presents a novel architecture named Memory-augmented Inter-Modality Bridge (MemBridge), which uses the learnable intermediate modality representations as the bridge for the interaction between videos and language. Specifically, in the transformer-based cross-modality encoder, we introduce the learnable bridge tokens as the interaction approach, which means the video and language tokens can only perceive information from bridge tokens and themselves. Moreover, a memory bank is proposed to store abundant modality interaction information for adaptively generating bridge tokens according to different cases, enhancing the capacity and robustness of the inter-modality bridge. Through pre-training, MemBridge explicitly models the representations for more sufficient inter-modality interaction. Comprehensive experiments show that our approach achieves competitive performance with previous methods on various downstream tasks including video-text retrieval, video captioning, and video question answering on multiple datasets, demonstrating the effectiveness of the proposed method. The code has been available at https://github.com/jahhaoyang/MemBridge." @default.
• W4384080288 created "2023-07-13" @default.
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• W4384080288 date "2023-01-01" @default.
• W4384080288 modified "2023-10-18" @default.
• W4384080288 title "MemBridge: Video-Language Pre-training with Memory-Augmented Inter-Modality Bridge" @default.
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• W4384080288 doi "https://doi.org/10.1109/tip.2023.3283916" @default.
• W4384080288 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37436853" @default.
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