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

arXiv: https://arxiv.org/abs/1912.12799 · HTML: https://mlsanity.com/paper/1912.12799/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Dec 2019.

## Built on by (most cited first, 2 of 2)

- [Progressive and Aligned Pose Attention Transfer for Person Image Generation](https://mlsanity.com/paper/2103.11622/) (2021)
- [Attention-based Adversarial Appearance Learning of Augmented Pedestrians](https://mlsanity.com/paper/2107.02673/) (2021)

## Abstract (arXiv)

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.