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• W2895845501 abstract "Video captioning is a challenging task owing to the complexity of understanding the copious visual information in videos and describing it using natural language. Different from previous work that encodes video information using a single flow, in this work, we introduce a novel Sibling Convolutional Encoder (SibNet) for video captioning, which utilizes a two-branch architecture to collaboratively encode videos. The first content branch encodes the visual content information of the video via autoencoder, and the second semantic branch encodes the semantic information by visual-semantic joint embedding. Then both branches are effectively combined with soft-attention mechanism and finally fed into a RNN decoder to generate captions. With our SibNet explicitly capturing both content and semantic information, the proposed method can better represent the rich information in videos. Extensive experiments on YouTube2Text and MSR-VTT datasets validate that the proposed architecture outperforms existing methods by a large margin across different evaluation metrics." @default.
• W2895845501 created "2018-10-26" @default.
• W2895845501 creator A5001916762 @default.
• W2895845501 creator A5083293468 @default.
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• W2895845501 date "2018-10-15" @default.
• W2895845501 modified "2023-10-16" @default.
• W2895845501 title "SibNet" @default.
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• W2895845501 doi "https://doi.org/10.1145/3240508.3240667" @default.
• W2895845501 hasPublicationYear "2018" @default.
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