Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Action Sets: Weakly Supervised Action Segmentation without Ordering
  Constraints   Action detection and temporal segmentation of actions in videos are topics of
increasing interest. While fully supervised systems have gained much attention
lately, full annotation of each action within the video is costly and
impractical for large amounts of video data. Thus, weakly supervised action
detection and temporal segmentation methods are of great importance. While most
works in this area assume an ordered sequence of occurring actions to be given,
our approach only uses a set of actions. Such action sets provide much less
supervision since neither action ordering nor the number of action occurrences
are known. In exchange, they can be easily obtained, for instance, from
meta-tags, while ordered sequences still require human annotation. We introduce
a system that automatically learns to temporally segment and label actions in a
video, where the only supervision that is used are action sets. An evaluation
on three datasets shows that our method still achieves good results although
the amount of supervision is significantly smaller than for other related
methods.

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The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
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The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
20
The categories of this paper are: acc-phys
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The categories of this paper are: atom-ph