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Recently, generative adversarial networks (GANs) have shown great advantagesin synthesizing images, leading to a boost of explorations of using fakedimages to augment data. This paper proposes a multimodal cascaded generativeadversarial networks (PMC-GANs) to generate realistic and diversifiedpedestrian images and augment pedestrian detection data. The generator of ourmodel applies a residual U-net structure, with multi-scale residual blocks toencode features, and attention residual blocks to help decode and rebuildpedestrian images. The model constructs in a coarse-to-fine fashion and adoptscascade structure, which is beneficial to produce high-resolution pedestrians.PMC-GANs outperforms baselines, and when used for data augmentation, itimproves pedestrian detection results.

PMC-GANs: Generating Multi-Scale High-Quality Pedestrian with Multimodal Cascaded GANs