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|a 10.1109/TPAMI.2019.2940007
|2 doi
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|a pubmed25n1024.xml
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|a eng
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| 100 |
1 |
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|a Liu, Sheng
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a SibNet
|b Sibling Convolutional Encoder for Video Captioning
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| 264 |
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|c 2021
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| 336 |
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|2 rdacarrier
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| 500 |
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|a Date Completed 29.09.2021
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| 500 |
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|a Date Revised 29.09.2021
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a Visual captioning, the task of describing an image or a video using one or few sentences, is a challenging task owing to the complexity of understanding the copious visual information and describing it using natural language. Motivated by the success of applying neural networks for machine translation, previous work applies sequence to sequence learning to translate videos into sentences. In this work, different from previous work that encodes visual information using a single flow, we introduce a novel Sibling Convolutional Encoder (SibNet) for visual captioning, which employs a dual-branch architecture to collaboratively encode videos. The first content branch encodes visual content information of the video with an autoencoder, capturing the visual appearance information of the video as other networks often do. While the second semantic branch encodes semantic information of the video via visual-semantic joint embedding, which brings complementary representation by considering the semantics when extracting features from videos. 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 model can better represent the rich information in videos. To validate the advantages of the proposed model, we conduct experiments on two benchmarks for video captioning, YouTube2Text and MSR-VTT. Our results demonstrate that the proposed SibNet consistently outperforms existing methods across different evaluation metrics
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| 650 |
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|a Journal Article
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| 650 |
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4 |
|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Ren, Zhou
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yuan, Junsong
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 43(2021), 9 vom: 09. Sept., Seite 3259-3272
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnas
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| 773 |
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|g volume:43
|g year:2021
|g number:9
|g day:09
|g month:09
|g pages:3259-3272
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|u http://dx.doi.org/10.1109/TPAMI.2019.2940007
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