Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Partially fake it till you make it: mixing real and fake thermal images
  for improved object detection   In this paper we propose a novel data augmentation approach for visual
content domains that have scarce training datasets, compositing synthetic 3D
objects within real scenes. We show the performance of the proposed system in
the context of object detection in thermal videos, a domain where 1) training
datasets are very limited compared to visible spectrum datasets and 2) creating
full realistic synthetic scenes is extremely cumbersome and expensive due to
the difficulty in modeling the thermal properties of the materials of the
scene. We compare different augmentation strategies, including state of the art
approaches obtained through RL techniques, the injection of simulated data and
the employment of a generative model, and study how to best combine our
proposed augmentation with these other techniques.Experimental results
demonstrate the effectiveness of our approach, and our single-modality detector
achieves state-of-the-art results on the FLIR ADAS dataset.

12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc