  Recent advances in deep learning have led to the development of accurate and
efficient models for various computer vision applications such as
classification, segmentation, and detection. However, learning highly accurate
models relies on the availability of large-scale annotated datasets. Due to
this, model performance drops drastically when evaluated on label-scarce
datasets having visually distinct images, termed as domain adaptation problem.
There is a plethora of works to adapt classification and segmentation models to
label-scarce target datasets through unsupervised domain adaptation.
Considering that detection is a fundamental task in computer vision, many
recent works have focused on developing novel domain adaptive detection
techniques. Here, we describe in detail the domain adaptation problem for
detection and present an extensive survey of the various methods. Furthermore,
we highlight strategies proposed and the associated shortcomings. Subsequently,
we identify multiple aspects of the problem that are most promising for future
research. We believe that this survey shall be valuable to the pattern
recognition experts working in the fields of computer vision, biometrics,
medical imaging, and autonomous navigation by introducing them to the problem,
and familiarizing them with the current status of the progress while providing
promising directions for future research.
