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We introduce the first active learning (AL) framework for high-accuracyinstance segmentation of moveable parts from RGB images of real indoor scenes.As with most human-in-the-loop approaches, the key criterion for success in ALis to minimize human effort while still attaining high performance. To thisend, we employ a transformer that utilizes a masked-attention mechanism tosupervise the active segmentation. To enhance the network tailored to moveableparts, we introduce a coarse-to-fine AL approach which first uses anobject-aware masked attention and then a pose-aware one, leveraging thehierarchical nature of the problem and a correlation between moveable parts andobject poses and interaction directions. Our method achieves close to fullyaccurate (96% and higher) segmentation results, with semantic labels, on realimages, with 82% time saving over manual effort, where the training dataconsists of only 11.45% annotated real photographs. At last, we contribute adataset of 2,550 real photographs with annotated moveable parts, demonstratingits superior quality and diversity over the current best alternatives.

Active Coarse-to-Fine Segmentation of Moveable Parts from Real Images