# Attention-based Adversarial Appearance Learning of Augmented Pedestrians

arXiv: https://arxiv.org/abs/2107.02673 · HTML: https://mlsanity.com/paper/2107.02673/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 29 references that are in the map.
- First posted on arXiv 6 Jul 2021.

## Builds on (most cited first, 20 of 20)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [CyCADA: Cycle-Consistent Adversarial Domain Adaptation](https://mlsanity.com/paper/1711.03213/) (2017)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [Playing for Data: Ground Truth from Computer Games](https://mlsanity.com/paper/1608.02192/) (2016)
- [Multimodal Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1804.04732/) (2018)
- [Learning from Simulated and Unsupervised Images through Adversarial Training](https://mlsanity.com/paper/1612.07828/) (2016)
- [A DIRT-T Approach to Unsupervised Domain Adaptation](https://mlsanity.com/paper/1802.08735/) (2018)
- [Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection](https://mlsanity.com/paper/1708.01642/) (2017)
- [Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsing](https://mlsanity.com/paper/1810.08705/) (2018)
- [All about Structure: Adapting Structural Information across Domains for Boosting Semantic Segmentation](https://mlsanity.com/paper/1903.12212/) (2019)
- [Unsupervised Attention-guided Image to Image Translation](https://mlsanity.com/paper/1806.02311/) (2018)
- [Semantic-aware Grad-GAN for Virtual-to-Real Urban Scene Adaption](https://mlsanity.com/paper/1801.01726/) (2018)
- [Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyond](https://mlsanity.com/paper/1804.02047/) (2018)
- [LCrowdV: Generating Labeled Videos for Simulation-based Crowd Behavior Learning](https://mlsanity.com/paper/1606.08998/) (2016)
- [Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters](https://mlsanity.com/paper/1703.06283/) (2017)
- [PMC-GANs: Generating Multi-Scale High-Quality Pedestrian with Multimodal Cascaded GANs](https://mlsanity.com/paper/1912.12799/) (2019)

## Built on by (most cited first, 1 of 1)

- [Diffusion Dataset Generation: Towards Closing the Sim2Real Gap for Pedestrian Detection](https://mlsanity.com/paper/2305.09401/) (2023)

## Abstract (arXiv)

Synthetic data became already an essential component of machine learning-based perception in the field of autonomous driving. Yet it still cannot replace real data completely due to the sim2real domain shift. In this work, we propose a method that leverages the advantages of the augmentation process and adversarial training to synthesize realistic data for the pedestrian recognition task. Our approach utilizes an attention mechanism driven by an adversarial loss to learn domain discrepancies and improve sim2real adaptation. Our experiments confirm that the proposed adaptation method is robust to such discrepancies and reveals both visual realism and semantic consistency. Furthermore, we evaluate our data generation pipeline on the task of pedestrian recognition and demonstrate that generated data resemble properties of the real domain.