Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: UntrimmedNets for Weakly Supervised Action Recognition and Detection   Current action recognition methods heavily rely on trimmed videos for model
training. However, it is expensive and time-consuming to acquire a large-scale
trimmed video dataset. This paper presents a new weakly supervised
architecture, called UntrimmedNet, which is able to directly learn action
recognition models from untrimmed videos without the requirement of temporal
annotations of action instances. Our UntrimmedNet couples two important
components, the classification module and the selection module, to learn the
action models and reason about the temporal duration of action instances,
respectively. These two components are implemented with feed-forward networks,
and UntrimmedNet is therefore an end-to-end trainable architecture. We exploit
the learned models for action recognition (WSR) and detection (WSD) on the
untrimmed video datasets of THUMOS14 and ActivityNet. Although our UntrimmedNet
only employs weak supervision, our method achieves performance superior or
comparable to that of those strongly supervised approaches on these two
datasets.

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The categories of this paper are: physics
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The categories of this paper are: adap-org
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The categories of this paper are: math
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
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The categories of this paper are: hep-lat
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The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
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The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: nlin