  We introduce associative embedding, a novel method for supervising
convolutional neural networks for the task of detection and grouping. A number
of computer vision problems can be framed in this manner including multi-person
pose estimation, instance segmentation, and multi-object tracking. Usually the
grouping of detections is achieved with multi-stage pipelines, instead we
propose an approach that teaches a network to simultaneously output detections
and group assignments. This technique can be easily integrated into any
state-of-the-art network architecture that produces pixel-wise predictions. We
show how to apply this method to both multi-person pose estimation and instance
segmentation and report state-of-the-art performance for multi-person pose on
the MPII and MS-COCO datasets.
