  Despite the substantial progress of active learning for image recognition,
there still lacks an instance-level active learning method specified for object
detection. In this paper, we propose Multiple Instance Active Object Detection
(MI-AOD), to select the most informative images for detector training by
observing instance-level uncertainty. MI-AOD defines an instance uncertainty
learning module, which leverages the discrepancy of two adversarial instance
classifiers trained on the labeled set to predict instance uncertainty of the
unlabeled set. MI-AOD treats unlabeled images as instance bags and feature
anchors in images as instances, and estimates the image uncertainty by
re-weighting instances in a multiple instance learning (MIL) fashion. Iterative
instance uncertainty learning and re-weighting facilitate suppressing noisy
instances, toward bridging the gap between instance uncertainty and image-level
uncertainty. Experiments validate that MI-AOD sets a solid baseline for
instance-level active learning. On commonly used object detection datasets,
MI-AOD outperforms state-of-the-art methods with significant margins,
particularly when the labeled sets are small. Code is available at
https://github.com/yuantn/MI-AOD.
