Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Predicting Motivations of Actions by Leveraging Text   Understanding human actions is a key problem in computer vision. However,
recognizing actions is only the first step of understanding what a person is
doing. In this paper, we introduce the problem of predicting why a person has
performed an action in images. This problem has many applications in human
activity understanding, such as anticipating or explaining an action. To study
this problem, we introduce a new dataset of people performing actions annotated
with likely motivations. However, the information in an image alone may not be
sufficient to automatically solve this task. Since humans can rely on their
lifetime of experiences to infer motivation, we propose to give computer vision
systems access to some of these experiences by using recently developed natural
language models to mine knowledge stored in massive amounts of text. While we
are still far away from fully understanding motivation, our results suggest
that transferring knowledge from language into vision can help machines
understand why people in images might be performing an action.

28
The categories of this paper are: alg-geom
25
The categories of this paper are: patt-sol
20
The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
16
The categories of this paper are: stat
34
The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th