To effectively interrogate UAV-based images for detecting objects of interest, such as humans, it is essential to acquire large-scale UAV-based datasets that include human instances with various poses captured from widely varying viewing angles. As a viable alternative to laborious and costly data curation, we introduce Progressive Transformation Learning (PTL), which gradually augments a training dataset by adding transformed virtual images with enhanced realism. Generally, a virtual2real transformation generator in the conditional GAN framework suffers from quality degradation when a large domain gap exists between real and virtual images. To deal with the domain gap, PTL takes a novel approach that progressively iterates the following three steps: 1) select a subset from a pool of virtual images according to the domain gap, 2) transform the selected virtual images to enhance realism, and 3) add the transformed virtual images to the training set while removing them from the pool. In PTL, accurately quantifying the domain gap is critical. To do that, we theoretically demonstrate that the feature representation space of a given object detector can be modeled as a multivariate Gaussian distribution from which the Mahalanobis distance between a virtual object and the Gaussian distribution of each object category in the representation space can be readily computed. Experiments show that PTL results in a substantial performance increase over the baseline, especially in the small data and the cross-domain regime.

翻译：为了有效测试基于无人机的图像以探测人类等受关注对象,必须获取大型基于无人机的图像,以探测人类等受关注对象,获得大型基于无人机的数据集,其中包括由各种不同角度收集的各种图像。作为艰苦和昂贵数据校正的可行替代办法,我们引入了进步转型学习(PTL),通过添加转换虚拟图像并强化现实主义,逐渐增加培训数据集。一般而言,在有条件的GAN框架中的虚拟2真实转换生成器在真实图像和虚拟图像之间存在巨大的域差时,质量会退化。为了处理域差,PTL采取了一种新颖的方法,逐步将以下三个步骤变异:1)从虚拟图像库中选择一个子集,根据域差选择一个子,2)将选定的虚拟图像转换为增强现实主义,3)在将虚拟图像从池中移出的同时,将虚拟图像添加到培训数据集。在PTL中,准确量化域差至关重要。为了做到这一点,我们理论上证明,特定天体探测器的特征显示空间可以建为多变式高星天体的分布,从中从中选择一个小天体分布,在虚拟空间中可以使每个实验天体的图像分布在虚拟空间中大大显示空间上显示空间的图像的大小。