  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 and its supervised
learning counterpart remains significant. By picking a small number of target
samples to annotate and using them in training, active domain adaptation tries
to mitigate this gap with minimum annotation expense. We propose an active
domain adaptation method in order to examine the efficiency of combining the
visible spectrum and thermal imagery modalities. When the domain gap is
considerably large as in the visible-to-thermal task, we may conclude that the
methods without explicit domain alignment cannot achieve their full potential.
To this end, we propose a spectral transfer guided active domain adaptation
method to select the most informative unlabeled target samples while aligning
source and target domains. We used the large-scale visible spectrum dataset
MS-COCO as the source domain and the thermal dataset FLIR ADAS as the target
domain to present the results of our method. Extensive experimental evaluation
demonstrates that our proposed method outperforms the state-of-the-art active
domain adaptation methods. The code and models are publicly available.
