Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Video action detection by learning graph-based spatio-temporal
  interactions   Action Detection is a complex task that aims to detect and classify human
actions in video clips. Typically, it has been addressed by processing
fine-grained features extracted from a video classification backbone. Recently,
thanks to the robustness of object and people detectors, a deeper focus has
been added on relationship modelling. Following this line, we propose a
graph-based framework to learn high-level interactions between people and
objects, in both space and time. In our formulation, spatio-temporal
relationships are learned through self-attention on a multi-layer graph
structure which can connect entities from consecutive clips, thus considering
long-range spatial and temporal dependencies. The proposed module is backbone
independent by design and does not require end-to-end training. Extensive
experiments are conducted on the AVA dataset, where our model demonstrates
state-of-the-art results and consistent improvements over baselines built with
different backbones. Code is publicly available at
https://github.com/aimagelab/STAGE_action_detection.

33
The categories of this paper are: plasm-ph
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
26
The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
30
The categories of this paper are: q-alg
32
The categories of this paper are: atom-ph
19
The categories of this paper are: eess
3
The categories of this paper are: physics