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Image caption generation with high-level image features

机译:具有高级图像功能的图像标题生成

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Recently, caption generation has raised a huge interests in images and videos. However, it is challenging for the models to select proper subjects in a complex background and generate desired captions in high-level vision tasks. Inspired by recent works, we propose a novel image captioning model based on high-level image features. We combine low-level information, such as image quality, with high-level features, such as motion classification and face recognition to detect attention regions of an image. We demonstrate that our attention model produces good performance in experiments on MSCOCO, Flickr 30K, PASCL and SBU datasets. (C) 2019 Elsevier B.V. All rights reserved.
机译:最近,字幕产生引起了人们对图像和视频的极大兴趣。但是,对于模型来说,在复杂的背景中选择合适的对象并在高级视觉任务中生成所需的字幕是具有挑战性的。受近期作品的启发,我们提出了一种基于高级图像特征的新颖图像字幕模型。我们将诸如图像质量之类的低级信息与诸如运动分类和人脸识别之类的高级特征相结合,以检测图像的关注区域。我们证明了我们的注意力模型在MSCOCO,Flickr 30K,PASCL和SBU数据集上的实验中产生了良好的性能。 (C)2019 Elsevier B.V.保留所有权利。

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