Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Predicting the Future: A Jointly Learnt Model for Action Anticipation   Inspired by human neurological structures for action anticipation, we present
an action anticipation model that enables the prediction of plausible future
actions by forecasting both the visual and temporal future. In contrast to
current state-of-the-art methods which first learn a model to predict future
video features and then perform action anticipation using these features, the
proposed framework jointly learns to perform the two tasks, future visual and
temporal representation synthesis, and early action anticipation. The joint
learning framework ensures that the predicted future embeddings are informative
to the action anticipation task. Furthermore, through extensive experimental
evaluations we demonstrate the utility of using both visual and temporal
semantics of the scene, and illustrate how this representation synthesis could
be achieved through a recurrent Generative Adversarial Network (GAN) framework.
Our model outperforms the current state-of-the-art methods on multiple
datasets: UCF101, UCF101-24, UT-Interaction and TV Human Interaction.

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The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con
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The categories of this paper are: hep-lat
18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
16
The categories of this paper are: stat
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex