Skip to footer

Sean Segal

Sean Segal
Sean is a research scientist at Uber ATG Toronto. He is also a Master’s student at the University of Toronto supervised by Professor Raquel Urtasun. His research interests include deep learning, computer vision and scenario recognition. Before joining the University of Toronto, Sean studied Computer Science & Economics at Brown University.

Research Papers

Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction

A. Jain, S. Casas, R. Liao, Y. Xiong, S. Feng, S. Segal, R. Urtasun
Our research shows that non-parametric distributions can capture extremely well the (erratic) pedestrian behavior. We propose Discrete Residual Flow, a convolutional neural network for human motion prediction that accurately models the temporal dependencies and captures the uncertainty inherent in long-range motion forecasting. In particular, our method captures multi-modal posteriors over future human motion very realistically. [PDF]
Conference on Neural Information Processing Systems (NeurIPS), 2019

Popular Articles