  Domain adaptation helps generalizing object detection models to target domain
data with distribution shift. It is often achieved by adapting with access to
the whole target domain data. In a more realistic scenario, target distribution
is often unpredictable until inference stage. This motivates us to explore
adapting an object detection model at test-time, a.k.a. test-time adaptation
(TTA). In this work, we approach test-time adaptive object detection (TTAOD)
from two perspective. First, we adopt a self-training paradigm to generate
pseudo labeled objects with an exponential moving average model. The pseudo
labels are further used to supervise adapting source domain model. As
self-training is prone to incorrect pseudo labels, we further incorporate
aligning feature distributions at two output levels as regularizations to
self-training. To validate the performance on TTAOD, we create benchmarks based
on three standard object detection datasets and adapt generic TTA methods to
object detection task. Extensive evaluations suggest our proposed method sets
the state-of-the-art on test-time adaptive object detection task.
