Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Revisiting hand-crafted feature for action recognition: a set of
  improved dense trajectories   We propose a feature for action recognition called Trajectory-Set (TS), on
top of the improved Dense Trajectory (iDT). The TS feature encodes only
trajectories around densely sampled interest points, without any appearance
features. Experimental results on the UCF50, UCF101, and HMDB51 action datasets
demonstrate that TS is comparable to state-of-the-arts, and outperforms many
other methods; for HMDB the accuracy of 85.4%, compared to the best accuracy of
80.2% obtained by a deep method. Our code is available on-line at
https://github.com/Gauffret/TrajectorySet .

18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
2
The categories of this paper are: math
3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol
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The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys
19
The categories of this paper are: eess
35
The categories of this paper are: mtrl-th