Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Exploring Feature Representation and Training strategies in Temporal
  Action Localization   Temporal action localization has recently attracted significant interest in
the Computer Vision community. However, despite the great progress, it is hard
to identify which aspects of the proposed methods contribute most to the
increase in localization performance. To address this issue, we conduct
ablative experiments on feature extraction methods, fixed-size feature
representation methods and training strategies, and report how each influences
the overall performance. Based on our findings, we propose a two-stage detector
that outperforms the state of the art in THUMOS14, achieving a mAP@tIoU=0.5
equal to 44.2%.

24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int