# Associative embedding: End-to-end learning for joint detection and grouping (AE)
Associative Embedding (NIPS'2017)
```bibtex
@inproceedings{newell2017associative,
title={Associative embedding: End-to-end learning for joint detection and grouping},
author={Newell, Alejandro and Huang, Zhiao and Deng, Jia},
booktitle={Advances in neural information processing systems},
pages={2277--2287},
year={2017}
}
```
## Abstract
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.
