Event Location: NSH 1507

Abstract: Progress in action recognition has been in large part due to advances in the features that drive learning-based methods.
However, the relative sparsity of training data and the risk of overfitting have made it difficult to directly search for good features. In this work we suggest using synthetic data to search for robust features that can more easily take advantage of limited data, rather than using the synthetic data directly as a substitute for real data.