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• W4206922148 abstract "Image captioning is an interesting and challenging task. The previously established image captioning approach is based mainly on the encoder-decoder architecture, but it suffers from problems such as inaccurate captioning information, and the generated captioning sentences are not sufficiently rich. This paper proposes a novel image captioning model that is based on a self-attention network and a scene graph relationship network. First, an improved self-attention network is added to the extraction of visual features to evaluate the effectiveness of image global information for image generation. Then, we design a visual intensity parameter to coordinate the strategies of visual features and language model for word generation. Finally, a graph convolutional network is designed to extract the relationships from the scene information to render the generated caption more exciting and to increase the accuracy of the fine-grained captioning. We demonstrated the satisfactory performance of the model on the MS-COCO and Flickr 30K datasets. The experimental results demonstrate that the proposed model realizes state-of-the-art performance." @default.
• W4206922148 created "2022-01-26" @default.
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• W4206922148 date "2021-12-01" @default.
• W4206922148 modified "2023-10-18" @default.
• W4206922148 title "Joint Scence Network and Attention-Guided for Image Captioning" @default.
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• W4206922148 doi "https://doi.org/10.1109/icdm51629.2021.00201" @default.
• W4206922148 hasPublicationYear "2021" @default.
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