  Object detectors often experience a drop in performance when new
environmental conditions are insufficiently represented in the training data.
This paper studies how to automatically fine-tune a pre-existing object
detector while exploring and acquiring images in a new environment without
relying on human intervention, i.e., in a self-supervised fashion. In our
setting, an agent initially explores the environment using a pre-trained
off-the-shelf detector to locate objects and associate pseudo-labels. By
assuming that pseudo-labels for the same object must be consistent across
different views, we devise a novel mechanism for producing refined predictions
from the consensus among observations. Our approach improves the off-the-shelf
object detector by 2.66% in terms of mAP and outperforms the current state of
the art without relying on ground-truth annotations.

Computer Vision and Pattern Recognition