Name
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 multi-person pose estimation and report state-of-the-art performance on the MPII and MS-COCO datasets. |
Year | Venue | Field |
|---|---|---|
2017 | ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 30 (NIPS 2017) | Embedding,Associative property,Pattern recognition,Computer science,End-to-end principle,Convolutional neural network,Segmentation,Network architecture,Pose,Artificial intelligence,Machine learning |
DocType | Volume | ISSN |
Conference | 30 | 1049-5258 |
Citations | PageRank | References |
6 | 0.40 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Newell, Alejandro | 1 | 28 | 1.80 |
| Zhiao Huang | 2 | 9 | 3.13 |
| Jia Deng | 3 | 10850 | 539.69 |