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Associative Embedding: End-to-End Learning for Joint Detection and Grouping
Alejandro Newell · Zhiao Huang · Jia Deng

Mon Dec 04 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #91 #None

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 multi-person pose estimation and report state-of-the-art performance on the MPII and MS-COCO datasets.

Author Information

Alejandro Newell (University of Michigan)
Zhiao Huang (IIIS, Tsinghua University)
Jia Deng (University of Michigan)

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