Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: VI-Net: View-Invariant Quality of Human Movement Assessment   We propose a view-invariant method towards the assessment of the quality of
human movements which does not rely on skeleton data. Our end-to-end
convolutional neural network consists of two stages, where at first a
view-invariant trajectory descriptor for each body joint is generated from RGB
images, and then the collection of trajectories for all joints are processed by
an adapted, pre-trained 2D CNN (e.g. VGG-19 or ResNeXt-50) to learn the
relationship amongst the different body parts and deliver a score for the
movement quality. We release the only publicly-available, multi-view,
non-skeleton, non-mocap, rehabilitation movement dataset (QMAR), and provide
results for both cross-subject and cross-view scenarios on this dataset. We
show that VI-Net achieves average rank correlation of 0.66 on cross-subject and
0.65 on unseen views when trained on only two views. We also evaluate the
proposed method on the single-view rehabilitation dataset KIMORE and obtain
0.66 rank correlation against a baseline of 0.62.

18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
24
The categories of this paper are: dg-ga
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
19
The categories of this paper are: eess
20
The categories of this paper are: acc-phys
28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph