  Prior to deployment, an object detector is trained on a dataset compiled from
a previous data collection campaign. However, the environment in which the
object detector is deployed will invariably evolve, particularly in outdoor
settings where changes in lighting, weather and seasons will significantly
affect the appearance of the scene and target objects. It is almost impossible
for all potential scenarios that the object detector may come across to be
present in a finite training dataset. This necessitates continuous updates to
the object detector to maintain satisfactory performance. Test-time domain
adaptation techniques enable machine learning models to self-adapt based on the
distributions of the testing data. However, existing methods mainly focus on
fully automated adaptation, which makes sense for applications such as
self-driving cars. Despite the prevalence of fully automated approaches, in
some applications such as surveillance, there is usually a human operator
overseeing the system's operation. We propose to involve the operator in
test-time domain adaptation to raise the performance of object detection beyond
what is achievable by fully automated adaptation. To reduce manual effort, the
proposed method only requires the operator to provide weak labels, which are
then used to guide the adaptation process. Furthermore, the proposed method can
be performed in a streaming setting, where each online sample is observed only
once. We show that the proposed method outperforms existing works,
demonstrating a great benefit of human-in-the-loop test-time domain adaptation.
Our code is publicly available at https://github.com/dzungdoan6/WSTTA
