Dr Abdulrahman Kerim

Stabilization plays a central role in improving the quality of videos. However, current methods perform poorly under adverse conditions. In this paper, we propose a synthetic-aware adverse weather video stabilization algorithm that dispenses real data for training, relying solely on synthetic data. Our approach leverages specially generated synthetic data to avoid the feature extraction issues faced by current methods. To achieve this, we present a novel data generator to produce the required training data with an automatic ground-truth extraction procedure. We also propose a new dataset, VSAC105Real, and compare our method to five recent video stabilization algorithms using two benchmarks. Our method generalizes well on real-world videos across all weather conditions and does not require large-scale synthetic training data. Implementations for our proposed video stabilization algorithm, generator, and datasets are available at https://github.com/A-Kerim/SyntheticData4VideoStabilization_WACV_2024.

Recent semantic segmentation models perform well under standard weather conditions and sufficient illumination but struggle with adverse weather conditions and nighttime. Collecting and annotating training data under these conditions is expensive, time-consuming, error-prone, and not always practical. Usually, synthetic data is used as a feasible data source to increase the amount of training data. However, just directly using synthetic data may actually harm the model’s performance under normal weather conditions while getting only small gains in adverse situations. Therefore, we present a novel architecture specifically designed for using synthetic training data for domain adaptation. We propose a simple yet powerful addition to DeepLabV3+ by using weather and time-of-the-day supervisors trained with multi-task learning, making it both weather and nighttime aware, which improves its mIoU accuracy by 14 percentage points on the ACDC dataset while maintaining a score of 75% mIoU on the Cityscapes dataset. Our code is available at https://github.com/lsmcolab/Semantic-Segmentation-under-Adverse-Conditions.

Description

The machine learning (ML) revolution has made our world unimaginable without its products and services. However, training ML models requires vast datasets, which entails a process plagued by high costs, errors, and privacy concerns associated with collecting and annotating real data. Synthetic data emerges as a promising solution to all these challenges. This book is designed to bridge theory and practice of using synthetic data, offering invaluable support for your ML journey. Synthetic Data for Machine Learning empowers you to tackle real data issues, enhance your ML models' performance, and gain a deep understanding of synthetic data generation. You’ll explore the strengths and weaknesses of various approaches, gaining practical knowledge with hands-on examples of modern methods, including Generative Adversarial Networks (GANs) and diffusion models. Additionally, you’ll uncover the secrets and best practices to harness the full potential of synthetic data. By the end of this book, you’ll have mastered synthetic data and positioned yourself as a market leader, ready for more advanced, cost-effective, and higher-quality data sources, setting you ahead of your peers in the next generation of ML.

What you will learn

- Understand real data problems, limitations, drawbacks, and pitfalls

- Harness the potential of synthetic data for data-hungry ML models

- Discover state-of-the-art synthetic data generation approaches and solutions

- Uncover synthetic data potential by working on diverse case studies

- Understand synthetic data challenges and emerging research topics

- Apply synthetic data to your ML projects successfully

Key benefits

- Avoid common data issues by identifying and solving them using synthetic data-based solutions

- Master synthetic data generation approaches to prepare for the future of machine learning

- Enhance performance, reduce budget, and stand out from competitors using synthetic data

- Purchase of the print or Kindle book includes a free PDF eBook