The field of computer vision is moving towards increased use of synthetic data to improve model performance and generalization. Synthetic data allows for systematic exploration of model boundaries and provides a way to challenge open-vocabulary object detectors. Recent research has shown that diffusion-based image augmentation can be used to generate synthetic data that closely represents real-world environments, such as snow-filled scenes. Additionally, synthetic data has been used to train convolutional neural networks (CNNs) for tasks such as particle size distribution measurement, achieving high accuracy and computational efficiency. The use of synthetic data also raises important questions about conditioning schemes and their effect on data quality. Noteworthy papers include:

• A study on challenging open-vocabulary object detectors with generated content in street scenes, which found that inpainting can challenge these models and provides valuable insights on how to improve them.
• A paper on diffusion-based image augmentation for semantic segmentation in outdoor robotics, which proposed a novel method for generating synthetic data that can be used to fine-tune models for deployment in specific environments.