  Learning semantic segmentation requires pixel-wise annotations, which can be
time-consuming and expensive. To reduce the annotation cost, we propose a
superpixel-based active learning (AL) framework, which collects a dominant
label per superpixel instead. To be specific, it consists of adaptive
superpixel and sieving mechanisms, fully dedicated to AL. At each round of AL,
we adaptively merge neighboring pixels of similar learned features into
superpixels. We then query a selected subset of these superpixels using an
acquisition function assuming no uniform superpixel size. This approach is more
efficient than existing methods, which rely only on innate features such as RGB
color and assume uniform superpixel sizes. Obtaining a dominant label per
superpixel drastically reduces annotators' burden as it requires fewer clicks.
However, it inevitably introduces noisy annotations due to mismatches between
superpixel and ground truth segmentation. To address this issue, we further
devise a sieving mechanism that identifies and excludes potentially noisy
annotations from learning. Our experiments on both Cityscapes and PASCAL VOC
datasets demonstrate the efficacy of adaptive superpixel and sieving
mechanisms.
