Semantic Attention Network for Image Captioning and Visual Question Answering Based on Image High-Level Semantic Attributes

Angelin Gladston, D. Balaji
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Abstract

The main challenge in the vision-to-language system is generation of the caption with a proper meaningful answer for a question and extracting even the minute details from the image. The main contributions in this paper are presenting an approach based on image high-level semantic attributes and local image features address the challenges of V2L tasks. Especially, the high-level semantic attributes information is used to reduce the semantic gap between images and text. A novel semantic attention network is designed to explore the mapping relationships between semantic attributes and image regions. The semantic attention network highlights the concept-related regions and selects the region-related concepts. Two special V2L tasks, image captioning and VQA, are addressed by the proposed approach. Improved BLEU score shows the proposed image captioning performs well. The experimental results show that the proposed model is effective for V2L tasks.
基于图像高级语义属性的图像字幕与视觉问答语义关注网络
视觉到语言系统的主要挑战是为问题生成具有适当意义的答案的标题,甚至从图像中提取微小的细节。本文的主要贡献是提出了一种基于图像高级语义属性和局部图像特征的方法来解决V2L任务的挑战。特别是利用高级语义属性信息来减小图像与文本之间的语义差距。设计了一种新的语义注意网络来探索语义属性与图像区域之间的映射关系。语义注意网络突出与概念相关的区域,选择与区域相关的概念。该方法解决了两个特殊的V2L任务,即图像字幕和VQA。改进的BLEU分数表明所提出的图像字幕性能良好。实验结果表明,该模型对V2L任务是有效的。
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