This paper aims to reduce the time to annotate images for the panoptic segmentation task, which requires annotating segmentation masks and class labels for all object instances and stuff regions. We formulate our approach as a collaborative process between an annotator and an automated assistant agent who take turns to jointly annotate an image using a predefined pool of segments. Actions performed by the annotator serve as a strong contextual signal. The assistant intelligently reacts to this signal by anticipating future actions of the annotator, which it then executes on its own. This reduces the amount of work required by the annotator. Experiments on the COCO panoptic dataset [Caesar18cvpr,Kirillov18arxiv,Lin14eccv} demonstrate that our approach is 17%-27% faster than the recent machine-assisted interface of [Andriluka18acmmm]. This corresponds to a 4x speed-up compared to the traditional manual polygon drawing [Russel08ijcv].
View on arXiv