Papers › Associative Embedding: End-to-End Learning for Joint Detection and Grouping

Associative Embedding: End-to-End Learning for Joint Detection and Grouping

16 Nov 2016NeurIPS 2017 12arXiv:1611.05424archive 2025-07-28

Alejandro Newell, Zhiao Huang, Jia Deng

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.

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