Using synthetic data for deep learning video recognition

In recent years, deep learning has completely revolutionized the fields of computer vision, speech recognition and natural language processing. Despite breakthroughs in all three fields, one common barrier for training neural networks to solve real-world problems remains the amount of labeled training data that is required to train a model. In some domains, like video understanding, gathering real world data can be prohibitively expensive and time consuming in the absence of innovative solutions. At TwentyBN, we solved this problem by building an in-house data factory for generating high-quality videos for neural networks to learn about the real world. We instruct crowd workers to record short video clips based on carefully predefined and highly specific descriptions.