Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Weakly But Deeply Supervised Occlusion-Reasoned Parametric Road Layouts   We propose an end-to-end network that takes a single perspective RGB image of
a complex road scene as input, to produce occlusion-reasoned layouts in
perspective space as well as a parametric bird's-eye-view (BEV) space. In
contrast to prior works that require dense supervision such as semantic labels
in perspective view, our method only requires human annotations for parametric
attributes that are cheaper and less ambiguous to obtain. To solve this
challenging task, our design is comprised of modules that incorporate inductive
biases to learn occlusion-reasoning, geometric transformation and semantic
abstraction, where each module may be supervised by appropriately transforming
the parametric annotations. We demonstrate how our design choices and proposed
deep supervision help achieve meaningful representations and accurate
predictions. We validate our approach on two public datasets, KITTI and
NuScenes, to achieve state-of-the-art results with considerably less human
supervision.

16
The categories of this paper are: stat
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
35
The categories of this paper are: mtrl-th
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph