ALWOD: Active Learning for Weakly-Supervised Object Detection
Yuting Wang Velibor Ilic Jiatong Li Branislav Kisacanin Vladimir Pavlovic
Object detection (OD), a crucial vision task, remains challenged by the lack
of large training datasets with precise object localization labels. In this
work, we propose ALWOD, a new framework that addresses this problem by fusing
active learning (AL) with weakly and semi-supervised object detection
paradigms. Because the performance of AL critically depends on the model
initialization, we propose a new auxiliary image generator strategy that
utilizes an extremely small labeled set, coupled with a large weakly tagged set
of images, as a warm-start for AL. We then propose a new AL acquisition
function, another critical factor in AL success, that leverages the
student-teacher OD pair disagreement and uncertainty to effectively propose the
most informative images to annotate. Finally, to complete the AL loop, we
introduce a new labeling task delegated to human annotators, based on selection
and correction of model-proposed detections, which is both rapid and effective
in labeling the informative images. We demonstrate, across several challenging
benchmarks, that ALWOD significantly narrows the gap between the ODs trained on
few partially labeled but strategically selected image instances and those that
rely on the fully-labeled data. Our code is publicly available on
https://github.com/seqam-lab/ALWOD.