Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DeepBbox: Accelerating Precise Ground Truth Generation for Autonomous
  Driving Datasets   Autonomous driving requires various computer vision algorithms, such as
object detection and tracking.Precisely-labeled datasets (i.e., objects are
fully contained in bounding boxes with only a few extra pixels) are preferred
for training such algorithms, so that the algorithms can detect exact locations
of the objects. However, it is very time-consuming and hence expensive to
generate precise labels for image sequences at scale. In this paper, we propose
DeepBbox, an algorithm that corrects loose object labels into right bounding
boxes to reduce human annotation efforts. We use Cityscapes dataset to show
annotation efficiency and accuracy improvement using DeepBbox. Experimental
results show that, with DeepBbox,we can increase the number of object edges
that are labeled automatically (within 1\% error) by 50% to reduce manual
annotation time.

11
The categories of this paper are: hep-lat
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
17
The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
24
The categories of this paper are: dg-ga
29
The categories of this paper are: funct-an
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph