Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adversarial Generative Grammars for Human Activity Prediction   In this paper we propose an adversarial generative grammar model for future
prediction. The objective is to learn a model that explicitly captures temporal
dependencies, providing a capability to forecast multiple, distinct future
activities. Our adversarial grammar is designed so that it can learn stochastic
production rules from the data distribution, jointly with its latent
non-terminal representations. Being able to select multiple production rules
during inference leads to different predicted outcomes, thus efficiently
modeling many plausible futures. The adversarial generative grammar is
evaluated on the Charades, MultiTHUMOS, Human3.6M, and 50 Salads datasets and
on two activity prediction tasks: future 3D human pose prediction and future
activity prediction. The proposed adversarial grammar outperforms the
state-of-the-art approaches, being able to predict much more accurately and
further in the future, than prior work.

31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
13
The categories of this paper are: quant-ph
30
The categories of this paper are: q-alg
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin