Summary of Aa-sgan: Adversarially Augmented Social Gan with Synthetic Data, by Mirko Zaffaroni et al.
AA-SGAN: Adversarially Augmented Social GAN with Synthetic Data
by Mirko Zaffaroni, Federico Signoretta, Marco Grangetto, Attilio Fiandrotti
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper addresses the challenge of accurately predicting pedestrian trajectories, crucial in applications like autonomous driving or service robotics. Existing deep generative models excel in this task, provided they’re trained with sufficient labelled trajectories. However, synthetically generated datasets are often ineffective due to their unrealistic representation of pedestrian motion. The authors introduce a method and architecture to augment synthetic trajectories at training time using an adversarial approach. This augmentation technique leads to significant performance gains when evaluated on real-world trajectories with state-of-the-art generative models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding a better way to predict where people will walk, which is important for things like self-driving cars and robots that help people. Right now, the best ways to do this use deep learning, but they need lots of labeled data to work well. This data is often fake and doesn’t look like real people walking. The authors have a new way to make the fake data look more like real people, which makes the predictions much better. |
Keywords
» Artificial intelligence » Deep learning
