  We introduce the first active learning (AL) model for high-accuracy instance
segmentation of moveable parts from RGB images of real indoor scenes.
Specifically, our goal is to obtain fully validated segmentation results by
humans while minimizing manual effort. To this end, we employ a transformer
that utilizes a masked-attention mechanism to supervise the active
segmentation. To enhance the network tailored to moveable parts, we introduce a
coarse-to-fine AL approach which first uses an object-aware masked attention
and then a pose-aware one, leveraging the hierarchical nature of the problem
and a correlation between moveable parts and object poses and interaction
directions. When applying our AL model to 2,000 real images, we obtain fully
validated moveable part segmentations with semantic labels, by only needing to
manually annotate 11.45% of the images. This translates to significant (60%)
time saving over manual effort required by the best non-AL model to attain the
same segmentation accuracy. At last, we contribute a dataset of 2,550 real
images with annotated moveable parts, demonstrating its superior quality and
diversity over the best alternatives.
