Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/151341
Title: Semantic-filtered Soft-Split-Aware video captioning with audio-augmented feature
Authors: Xu, Yuecong
Yang, Jianfei
Mao, Kezhi
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Xu, Y., Yang, J. & Mao, K. (2019). Semantic-filtered Soft-Split-Aware video captioning with audio-augmented feature. Neurocomputing, 357, 24-35. https://dx.doi.org/10.1016/j.neucom.2019.05.027
Journal: Neurocomputing
Abstract: Automatic video description, or video captioning, is a challenging yet much attractive task. It aims to combine video with text. Multiple methods have been proposed based on neural networks, utilizing Convolutional Neural Networks (CNN) to extract features, and Recurrent Neural Networks (RNN) to encode and decode videos to generate descriptions. Previously, a number of methods used in video captioning task are motivated by image captioning approaches. However, videos carry much more information than images. This increases the difficulty of video captioning task. Current methods commonly lack the ability to utilize the additional information provided by videos, especially the semantic and structural information of the videos. To address the above shortcoming, we propose a Semantic-Filtered Soft-Split-Aware-Gated LSTM (SF-SSAG-LSTM) model, that would improve video captioning quality by combining semantic concepts with audio-augmented feature extracted from input videos, while understanding the underlying structure of input videos. In the experiments, we quantitatively evaluate the performance of our model which matches other prominent methods on three benchmark datasets. We also qualitatively examine the result of our model, and show that our generated descriptions are more detailed and logical.
URI: https://hdl.handle.net/10356/151341
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2019.05.027
Rights: © 2019 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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