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.
Papers
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Proceedings ArticleDOI
Decorrelated Batch Normalization
TL;DR: Decorrelated batch normalization (DBN) as mentioned in this paper whitens the activations to accelerate the training of deep models by centering and scaling activations within mini-batches.
Proceedings ArticleDOI
Scalable multi-label annotation
TL;DR: An algorithm that exploits correlation, hierarchy, and sparsity of the label distribution is proposed that results in up to 6x reduction in human computation time compared to the naive method of querying a human annotator for the presence of every object in every image.
Proceedings ArticleDOI
Towards Fairer Datasets: Filtering and Balancing the Distribution of the People Subtree in the ImageNet Hierarchy
TL;DR: This paper examines ImageNet, a large-scale ontology of images that has spurred the development of many modern computer vision methods, and considers three key factors within the person subtree of ImageNet that may lead to problematic behavior in downstream computer vision technology.
Book ChapterDOI
Towards Scalable Dataset Construction: An Active Learning Approach
TL;DR: This work presents a discriminative learning process which employs active, online learning to quickly classify many images with minimal user input, and demonstrates precision which is often superior to the state-of-the-art, with scalability which exceeds previous work.
Posted Content
CornerNet-Lite: Efficient Keypoint Based Object Detection
TL;DR: CornerNet-Lite is a combination of two efficient variants of CornerNet: Corner net-Saccade, which uses an attention mechanism to eliminate the need for exhaustively processing all pixels of the image, and CornerNet-Squeeze, which introduces a new compact backbone architecture that addresses the two critical use cases in efficient object detection.