用于空对地图像合成的语义感知网络
Semantic-aware Network for Aerial-to-Ground Image Synthesis
AI总结：
针对空对地图像合成中场景布局与物体表示差异大的问题，提出语义注意特征变换模块和基于分割网络的分类平衡语义感知损失，实验验证框架有效。
摘要：
空对地图像合成是一个新兴且具有挑战性的问题，旨在根据航空图像合成地面图像。由于航空图像与地面图像在布局和物体表示上存在巨大差异，现有方法通常无法将航空场景的组成部分迁移到地面场景中。本文提出一种新颖框架，通过增强结构对齐和语义感知来探索这些挑战。作者引入了一个新颖的语义注意特征变换模块，该模块通过将航空特征与地面布局对齐，能够重建复杂的地理结构。此外，作者利用预训练分割网络提出了语义感知损失函数。通过分别计算不同类别的损失并对其进行平衡，该网络被强制要求合成跨越多个类别的真实物体。包括与先前方法比较以及消融研究在内的大量实验，从定性和定量两方面证明了所提出框架的有效性。
英文摘要：
Aerial-to-ground image synthesis is an emerging and challenging problem that aims to synthesize a ground image from an aerial image. Due to the highly different layout and object representation between the aerial and ground images, existing approaches usually fail to transfer the components of the aerial scene into the ground scene. In this paper, we propose a novel framework to explore the challenges by imposing enhanced structural alignment and semantic awareness. We introduce a novel semantic-attentive feature transformation module that allows to reconstruct the complex geographic structures by aligning the aerial feature to the ground layout. Furthermore, we propose semantic-aware loss functions by leveraging a pre-trained segmentation network. The network is enforced to synthesize realistic objects across various classes by separately calculating losses for different classes and balancing them. Extensive experiments including comparisons with previous methods and ablation studies show the effectiveness of the proposed framework both qualitatively and quantitatively.
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。
