Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Untrimmed Video Classification for Activity Detection: submission to
  ActivityNet Challenge   Current state-of-the-art human activity recognition is focused on the
classification of temporally trimmed videos in which only one action occurs per
frame. We propose a simple, yet effective, method for the temporal detection of
activities in temporally untrimmed videos with the help of untrimmed
classification. Firstly, our model predicts the top k labels for each untrimmed
video by analysing global video-level features. Secondly, frame-level binary
classification is combined with dynamic programming to generate the temporally
trimmed activity proposals. Finally, each proposal is assigned a label based on
the global label, and scored with the score of the temporal activity proposal
and the global score. Ultimately, we show that untrimmed video classification
models can be used as stepping stone for temporal detection.

19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
28
The categories of this paper are: alg-geom
8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
6
The categories of this paper are: astro-ph
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an