Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Toward Accurate Person-level Action Recognition in Videos of Crowded
  Scenes   Detecting and recognizing human action in videos with crowded scenes is a
challenging problem due to the complex environment and diversity events. Prior
works always fail to deal with this problem in two aspects: (1) lacking
utilizing information of the scenes; (2) lacking training data in the crowd and
complex scenes. In this paper, we focus on improving spatio-temporal action
recognition by fully-utilizing the information of scenes and collecting new
data. A top-down strategy is used to overcome the limitations. Specifically, we
adopt a strong human detector to detect the spatial location of each frame. We
then apply action recognition models to learn the spatio-temporal information
from video frames on both the HIE dataset and new data with diverse scenes from
the internet, which can improve the generalization ability of our model.
Besides, the scenes information is extracted by the semantic segmentation model
to assistant the process. As a result, our method achieved an average 26.05
wf\_mAP (ranking 1st place in the ACM MM grand challenge 2020: Human in
Events).

8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
19
The categories of this paper are: eess
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
24
The categories of this paper are: dg-ga
2
The categories of this paper are: math
34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin