  Over the years various methods have been proposed for the problem of object
detection. Recently, we have witnessed great strides in this domain owing to
the emergence of powerful deep neural networks. However, there are typically
two main assumptions common among these approaches. First, the model is trained
on a fixed training set and is evaluated on a pre-recorded test set. Second,
the model is kept frozen after the training phase, so no further updates are
performed after the training is finished. These two assumptions limit the
applicability of these methods to real-world settings. In this paper, we
propose Interactron, a method for adaptive object detection in an interactive
setting, where the goal is to perform object detection in images observed by an
embodied agent navigating in different environments. Our idea is to continue
training during inference and adapt the model at test time without any explicit
supervision via interacting with the environment. Our adaptive object detection
model provides a 7.2 point improvement in AP (and 12.7 points in AP50) over
DETR, a recent, high-performance object detector. Moreover, we show that our
object detection model adapts to environments with completely different
appearance characteristics, and performs well in them. The code is available
at: https://github.com/allenai/interactron .
