A Variational Auto-Encoder Model for Stochastic Point Processes

Nazanin Mehrasa; Akash Abdu Jyothi; Thibaut Durand; Jiawei He; Leonid Sigal; Greg Mori

CVPR 2019poster70 citations

A Variational Auto-Encoder Model for Stochastic Point Processes

Nazanin Mehrasa, Akash Abdu Jyothi, Thibaut Durand, Jiawei He, Leonid Sigal, Greg Mori

Abstract

We propose a novel probabilistic generative model for action sequences. The model is termed the Action Point Process VAE (APP-VAE), a variational auto-encoder that can capture the distribution over the times and categories of action sequences. Modeling the variety of possible action sequences is a challenge, which we show can be addressed via the APP-VAE's use of latent representations and non-linear functions to parameterize distributions over which event is likely to occur next in a sequence and at what time. We empirically validate the efficacy of APP-VAE for modeling action sequences on the MultiTHUMOS and Breakfast datasets.

BibTeX

@inproceedings{cvpr2019_avariationalauto,
  title = {A Variational Auto-Encoder Model for Stochastic Point Processes},
  author = {Nazanin Mehrasa and Akash Abdu Jyothi and Thibaut Durand and Jiawei He and Leonid Sigal and Greg Mori},
  booktitle = {CVPR 2019},
  year = {2019}
}