SemIE：语义感知的图像外推
SemIE: Semantically-aware Image Extrapolation
摘要：
我们提出了一个语义感知的新范式来执行图像外推，使新的对象实例的添加。所有以前的方法都局限于它们的外推能力，仅扩展图像中已经存在的对象。然而，我们提出的方法不仅关注（i）扩展已经存在的对象，而且关注（ii）基于上下文在扩展区域中添加新对象。为此，对于一个给定的图像，我们首先获得一个对象分割图使用国家的最先进的语义分割方法。因此，所获得的分割图被馈送到网络中以计算外推的语义分割和对应的全景分割图。进一步利用输入图像和所获得的分割图来生成最终外推图像。我们在Cityscapes和ADE 20 K-bedroom数据集上进行了实验，结果表明，我们的方法在FID和对象共现统计的相似性方面优于所有基线。
英文摘要：
We propose a semantically-aware novel paradigm to perform image extrapolation that enables the addition of new object instances. All previous methods are limited in their capability of extrapolation to merely extending the already existing objects in the image. However, our proposed approach focuses not only on (i) extending the already present objects but also on (ii) adding new objects in the extended region based on the context. To this end, for a given image, we first obtain an object segmentation map using a state-of-the-art semantic segmentation method. The, thus, obtained segmentation map is fed into a network to compute the extrapolated semantic segmentation and the corresponding panoptic segmentation maps. The input image and the obtained segmentation maps are further utilized to generate the final extrapolated image. We conduct experiments on Cityscapes and ADE20K-bedroom datasets and show that our method outperforms all baselines in terms of FID, and similarity in object co-occurrence statistics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
