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.

通过基于无人飞行器图像的大规模数据集，采用逐渐增加虚拟图像的现实感的渐进式变换学习方法，通过 multivariate Gaussian distribution 建模来准确计算对象探测器中虚拟对象与高斯分布之间的马氏距离，以此减小牺牲精度的条件 GAN 框架在大间隔域条件下近似度的失真，从而提高在小数据和交叉域中的性能。