Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Label-Aware Ranked Loss for robust People Counting using Automotive
  in-cabin Radar   In this paper, we introduce the Label-Aware Ranked loss, a novel metric loss
function. Compared to the state-of-the-art Deep Metric Learning losses, this
function takes advantage of the ranked ordering of the labels in regression
problems. To this end, we first show that the loss minimises when datapoints of
different labels are ranked and laid at uniform angles between each other in
the embedding space. Then, to measure its performance, we apply the proposed
loss on a regression task of people counting with a short-range radar in a
challenging scenario, namely a vehicle cabin. The introduced approach improves
the accuracy as well as the neighboring labels accuracy up to 83.0% and 99.9%:
An increase of 6.7%and 2.1% on state-of-the-art methods, respectively.

16
The categories of this paper are: stat
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
20
The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg