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Object detectors often experience a drop in performance when newenvironmental conditions are insufficiently represented in the training data.This paper studies how to automatically fine-tune a pre-existing objectdetector while exploring and acquiring images in a new environment withoutrelying on human intervention, i.e., in an utterly self-supervised fashion. Inour setting, an agent initially learns to explore the environment using apre-trained off-the-shelf detector to locate objects and associatepseudo-labels. By assuming that pseudo-labels for the same object must beconsistent across different views, we learn an exploration policy mining hardsamples and we devise a novel mechanism for producing refined predictions fromthe consensus among observations. Our approach outperforms the currentstate-of-the-art, and it closes the performance gap against a fully supervisedsetting without relying on ground-truth annotations. We also compare variousexploration policies for the agent to gather more informative observations.Code and dataset will be made available upon paper acceptance

Look around and learn: self-improving object detection by exploration