J
Jia Deng
Researcher at Princeton University
Publications - 158
Citations - 110718
Jia Deng is an academic researcher from Princeton University. The author has contributed to research in topics: Computer science & Object detection. The author has an hindex of 50, co-authored 148 publications receiving 73461 citations. Previous affiliations of Jia Deng include University of Michigan & Carnegie Mellon University.
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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.
Book ChapterDOI
Cornernet: Detecting objects as paired keypoints
TL;DR: CornerNet as mentioned in this paper detects an object bounding box as a pair of keypoints, the top-left corner and the bottom-right corner, using a single convolution neural network.
Book ChapterDOI
RAFT: Recurrent All-Pairs Field Transforms for Optical Flow
Zachary Teed,Jia Deng +1 more
TL;DR: RAFT as mentioned in this paper extracts per-pixel features, builds multi-scale 4D correlation volumes for all pairs of pixels, and iteratively updates a flow field through a recurrent unit that performs lookups on the correlation volumes.
Posted Content
CornerNet: Detecting Objects as Paired Keypoints
TL;DR: CornerNet, a new approach to object detection where an object bounding box is detected as a pair of keypoints, the top-left corner and the bottom-right corner, using a single convolution neural network, is proposed.
Proceedings ArticleDOI
Rethinking the Faster R-CNN Architecture for Temporal Action Localization
Yu-Wei Chao,Sudheendra Vijayanarasimhan,Bryan Seybold,David A. Ross,Jia Deng,Rahul Sukthankar +5 more
TL;DR: TAL-Net as mentioned in this paper improves receptive field alignment using a multi-scale architecture that can accommodate extreme variation in action durations and better exploit the temporal context of actions for both proposal generation and action classification by appropriately extending receptive fields.