Augment it Maybe?: Improving Deep Vision Models with Adversarial Scene Text Augmentation

Image data augmentation has been regarded as a reliable and effective way to increase the data available for training. With the advent and rise of Generative AI, generative data augmentation has been shown to realize even better gains in performance for downstream tasks. However, these performance gains are often the cause of "extra information"...

Enriching Diversity of Synthetic Images for Person Detection

Camera-based patient monitoring is undergoing rapid adoption in the healthcare sector with the recent COVID-19 pandemic acting as a catalyst. It offers round-the-clock monitoring of patients in clinical units (e.g. ICUs, ORs), or at their homes through installed cameras, enabling timely, pre-emptive care. These are powered by Computer Vision...

Image-to-Image Translation of Synthetic Samples for Rare Classes

The natural world is long-tailed: rare classes are observed orders of magnitudes less frequently than common ones, leading to highly-imbalanced data where rare classes can have only handfuls of examples. Learning from few examples is a known challenge for deep learning based classification algorithms, and is the focus of the field of low-shot...

Synthetic Waste Generator for Classification Training

As the population increases so does the waste that is generated. Manually recycling waste is expensive and slow. Computer Vision (CV) solutions aim to make this less expensive and faster. Lots of data of this waste (thousands of images) is needed to train these CV solutions. This project, called Synthetic Waste Generator (SWaG) can create...