Spectral Transfer Guided Active Domain Adaptation For Thermal Imagery. (arXiv:2304.07031v1 [cs.CV])

The exploitation of visible spectrum datasets has led deep networks to show
remarkable success. However, real-world tasks include low-lighting conditions
which arise performance bottlenecks for models trained on large-scale RGB image
datasets. Thermal IR cameras are more robust against such conditions.
Therefore, the usage of thermal imagery in real-world applications can be
useful. Unsupervised domain adaptation (UDA) allows transferring information
from a source domain to a fully unlabeled target domain. Despite substantial
improvements in UDA, the performance gap between UDA …

applications arxiv cameras datasets domain adaptation exploitation gap image image datasets information lighting low networks performance scale show spectrum success supervised learning transfer unsupervised usage world