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

We introduce the first active learning (AL) framework for high-accuracy instance 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 AL is to minimize human effort while still attaining high performance. 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. Our method achieves close to fully accurate (96% and higher) segmentation results, with semantic labels, on real images, with 82% time saving over manual effort, where the training data consists of only 11.45% annotated real photographs. At last, we contribute a dataset of 2,550 real photographs with annotated moveable parts, demonstrating its superior quality and diversity over the current best alternatives.