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Generative Hybrid Representations for Activity Forecasting with No-Regret Learning | | Jiaqi Guan
; Ye Yuan
; Kris M. Kitani
; Nicholas Rhinehart
; | | Date: |
12 Apr 2019 | | Abstract: | Automatically reasoning about future human behaviors is a difficult problem
with significant practical applications to assistive systems. Part of this
difficulty stems from learning systems’ inability to represent all kinds of
behaviors. Some behaviors, such as motion, are best described with continuous
representations, whereas others, such as picking up a cup, are best described
with discrete representations. Furthermore, human behavior is generally not
fixed: people can change their habits and routines. This suggests these systems
must be able to learn and adapt continuously. In this work, we develop an
efficient deep generative model to jointly forecast a person’s future discrete
actions and continuous motions. On a large-scale egocentric dataset,
EPIC-KITCHENS, we observe our method generates high-quality and diverse samples
while exhibiting better generalization than related generative models. Finally,
we propose a variant to continually learn our model from streaming data,
observe its practical effectiveness, and theoretically justify its learning
efficiency. | | Source: | arXiv, 1904.6250 | | Services: | Forum | Review | PDF | Favorites | |
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