  Recently, generative adversarial networks (GANs) have shown great advantages
in synthesizing images, leading to a boost of explorations of using faked
images to augment data. This paper proposes a multimodal cascaded generative
adversarial networks (PMC-GANs) to generate realistic and diversified
pedestrian images and augment pedestrian detection data. The generator of our
model applies a residual U-net structure, with multi-scale residual blocks to
encode features, and attention residual blocks to help decode and rebuild
pedestrian images. The model constructs in a coarse-to-fine fashion and adopts
cascade structure, which is beneficial to produce high-resolution pedestrians.
PMC-GANs outperforms baselines, and when used for data augmentation, it
improves pedestrian detection results.
