Accenture Labs India - Research Intern (Oct 2021)

I developed a framework to enhance the diversity of a small in-lab gesture video dataset by researching state-of-the-art synthetic video generation methods and implementing augmentations using OpenCV and Pillow. The automated framework increased video samples by 100 times through systematic application of blurring, random cropping, and affine transformations. Using the synthetic dataset, I trained and evaluated deep learning models like MocoGAN and TecoGAN, identifying GAN-based methods as optimal for limited gold-standard datasets. This approach led to a 10% relative improvement in classification performance over competitive baselines. Additionally, I identified the limitations of mechanical augmentations and recommended diffusion models for more sophisticated data augmentation.