Attention-Based Convolutional LSTM for Describing Video Article Swipe

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Zhongyu Liu , Chen Tian , Enjie Ding , Ya‐Feng Liu , Wanli Yu ·

YOU? · · 2020 · Open Access · · DOI: https://doi.org/10.1109/access.2020.3010872

Video description technique has been widely used in the computer community for many applications. The typical approaches are mainly based on the encode-decode framework: the fixed-length video representation vectors are extracted by the encoder using the upper layer output of pre-trained convolutional neural networks (CNNs); The decoder uses the recurrent neural networks to generate a textual sentence. However, the upper layers of convolutional neural networks contain low-resolution, semantically strong, while the lower layers contain high-resolution, semantically weak features. In the existing method, the multi-scale information of CNNs is hardly considered to be used in the video description. Ignoring this information will lead to the problem that the video description is not detailed and comprehensive. This paper applies the hierarchical convolutional long short-term memory (ConvLSTM) in the encode-decode framework to conduct feature extraction of the upper and lower layers in CNNs. Moreover, multiple network structures are designed to explore the Spatio-temporal feature extraction performance of ConvLSTM, which can approach the optimal accuracy in the three-layer ConvLSTM. In order to efficiently improve the language quality of video description, the attention mechanism focuses on visual feature output by ConvLSTM. The extensive experimental results demonstrate that the proposed method outperforms the existing approaches.

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Computer Science

Convolutional Neural Network

Artificial Intelligence

Recurrent Neural Network

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Computer science ENCODE Convolutional neural network Artificial intelligence Encoder Feature extraction Sentence Feature (linguistics) Representation (politics) Encoding (memory) Layer (electronics) Pattern recognition (psychology) Semantics (computer science) Recurrent neural network Artificial neural network Gene Philosophy Programming language Law Biochemistry Operating system Politics Chemistry Political science Organic chemistry Linguistics

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Type: article
Language: en
Landing Page: https://doi.org/10.1109/access.2020.3010872
PDF: https://ieeexplore.ieee.org/ielx7/6287639/8948470/09145733.pdf
OA Status: gold
Cited By: 6
References: 65
Related Works: 10
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DOI: https://doi.org/10.1109/access.2020.3010872

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Title: Attention-Based Convolutional LSTM for Describing Video

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Language: en

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Publication year: 2020

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Publication date: 2020-01-01

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Authors: Zhongyu Liu, Chen Tian, Enjie Ding, Ya‐Feng Liu, Wanli Yu

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Landing page: https://doi.org/10.1109/access.2020.3010872

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Concepts: Computer science, ENCODE, Convolutional neural network, Artificial intelligence, Encoder, Feature extraction, Sentence, Feature (linguistics), Representation (politics), Encoding (memory), Layer (electronics), Pattern recognition (psychology), Semantics (computer science), Recurrent neural network, Artificial neural network, Gene, Philosophy, Programming language, Law, Biochemistry, Operating system, Politics, Chemistry, Political science, Organic chemistry, Linguistics

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Cited by: 6

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Citations by year (recent): 2025: 1, 2024: 2, 2022: 2, 2021: 1

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Related works (count): 10

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