Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Mining YouTube - A dataset for learning fine-grained action concepts
  from webly supervised video data   Action recognition is so far mainly focusing on the problem of classification
of hand selected preclipped actions and reaching impressive results in this
field. But with the performance even ceiling on current datasets, it also
appears that the next steps in the field will have to go beyond this fully
supervised classification. One way to overcome those problems is to move
towards less restricted scenarios. In this context we present a large-scale
real-world dataset designed to evaluate learning techniques for human action
recognition beyond hand-crafted datasets. To this end we put the process of
collecting data on its feet again and start with the annotation of a test set
of 250 cooking videos. The training data is then gathered by searching for the
respective annotated classes within the subtitles of freely available videos.
The uniqueness of the dataset is attributed to the fact that the whole process
of collecting the data and training does not involve any human intervention. To
address the problem of semantic inconsistencies that arise with this kind of
training data, we further propose a semantical hierarchical structure for the
mined classes.

5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
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The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
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The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
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The categories of this paper are: cmp-lg
10
The categories of this paper are: q-bio
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The categories of this paper are: math-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: adap-org