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Dual-CNN: A Convolutional language decoder for paragraph image captioning
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DOI：10.1016/j.neucom.2020.02.041.png)
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Abstract
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The task of paragraph image captioning aims to generate a coherent paragraph describing a given image. However, due to their limited ability to capture long-term dependency, recurrent neural network or long-short term memory based decoders could hardly generate satisfactory textual descriptions with a long paragraph. In addition, the training inefficiency in the sequential decoders is significantly observed. Motivated by the advantage of convolutional neural network (i.e., CNN), in this paper, we propose a Dual-CNN decoder with long-term memory ability and parallel computation, which can produce a semantically coherent paragraph for an image. Our Dual-CNN model is evaluated on the Stanford image-paragraph dataset. Extensive experiments demonstrate that our Dual-CNN achieves comparable results compared with state-of-the-art models. Furthermore, the diversity and coherence of generated paragraphs are analyzed to show the superiority of our approach. (C) 2020 Elsevier B.V. All rights reserved.
Keywords:
Deep learning
Language and vision
Convolutional neural networks
Image captioning
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