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Alejandro Newell
Researcher at Princeton University
Publications - 14
Citations - 7426
Alejandro Newell is an academic researcher from Princeton University. The author has contributed to research in topics: Pose & Convolutional neural network. The author has an hindex of 7, co-authored 13 publications receiving 5813 citations. Previous affiliations of Alejandro Newell include University of Michigan.
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Stacked Hourglass Networks for Human Pose Estimation
TL;DR: This work introduces a novel convolutional network architecture for the task of human pose estimation that is described as a “stacked hourglass” network based on the successive steps of pooling and upsampling that are done to produce a final set of predictions.
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Stacked Hourglass Networks for Human Pose Estimation
TL;DR: Stacked hourglass networks as mentioned in this paper were proposed for human pose estimation, where features are processed across all scales and consolidated to best capture the various spatial relationships associated with the body, and repeated bottom-up, top-down processing with intermediate supervision is critical to improving the performance of the network.
Proceedings Article
Associative Embedding: End-to-End Learning for Joint Detection and Grouping
TL;DR: In this article, associative embedding is used to supervise convolutional neural networks for the task of detection and grouping, which can be easily integrated into any state-of-the-art network architecture that produces pixel-wise predictions.
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Associative Embedding:End-to-End Learning for Joint Detection and Grouping
TL;DR: Associative embedding is introduced, a novel method for supervising convolutional neural networks for the task of detection and grouping for multi-person pose estimation and state-of-the-art performance on the MPII and MS-COCO datasets is reported.
Proceedings Article
Pixels to Graphs by Associative Embedding
Alejandro Newell,Jia Deng +1 more
TL;DR: A method for training a convolutional neural network such that it takes in an input image and produces a full graph definition and is done end-to-end in a single stage with the use of associative embeddings.