A Variational Auto-Encoder Model for Stochastic Point Processes

Nazanin Mehrasa, Akash Abdu Jyothi, Thibaut Durand, Jiawei He, Leonid Sigal, Greg Mori; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 3165-3174

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.

Related Material


[pdf]
[bibtex]
@InProceedings{Mehrasa_2019_CVPR,
author = {Mehrasa, Nazanin and Jyothi, Akash Abdu and Durand, Thibaut and He, Jiawei and Sigal, Leonid and Mori, Greg},
title = {A Variational Auto-Encoder Model for Stochastic Point Processes},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}