Social Image Captioning: Exploring Visual Attention and User Attention
AbstractImage captioning with a natural language has been an emerging trend. However, the social image, associated with a set of user-contributed tags, has been rarely investigated for a similar task. The user-contributed tags, which could reflect the user attention, have been neglected in conventional image captioning. Most existing image captioning models cannot be applied directly to social image captioning. In this work, a dual attention model is proposed for social image captioning by combining the visual attention and user attention simultaneously.Visual attention is used to compress a large mount of salient visual information, while user attention is applied to adjust the description of the social images with user-contributed tags. Experiments conducted on the Microsoft (MS) COCO dataset demonstrate the superiority of the proposed method of dual attention. View Full-Text
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Wang, L.; Chu, X.; Zhang, W.; Wei, Y.; Sun, W.; Wu, C. Social Image Captioning: Exploring Visual Attention and User Attention. Sensors 2018, 18, 646.
Wang L, Chu X, Zhang W, Wei Y, Sun W, Wu C. Social Image Captioning: Exploring Visual Attention and User Attention. Sensors. 2018; 18(2):646.Chicago/Turabian Style
Wang, Leiquan; Chu, Xiaoliang; Zhang, Weishan; Wei, Yiwei; Sun, Weichen; Wu, Chunlei. 2018. "Social Image Captioning: Exploring Visual Attention and User Attention." Sensors 18, no. 2: 646.
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