J
Jeff Donahue
Researcher at Google
Publications - 54
Citations - 96514
Jeff Donahue is an academic researcher from Google. The author has contributed to research in topics: Object detection & Convolutional neural network. The author has an hindex of 42, co-authored 54 publications receiving 82226 citations. Previous affiliations of Jeff Donahue include University of California, Berkeley & University of California.
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Proceedings ArticleDOI
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
TL;DR: RCNN as discussed by the authors combines CNNs with bottom-up region proposals to localize and segment objects, and when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost.
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Rich feature hierarchies for accurate object detection and semantic segmentation
TL;DR: This paper proposes a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 30% relative to the previous best result on VOC 2012 -- achieving a mAP of 53.3%.
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Caffe: Convolutional Architecture for Fast Feature Embedding
Yangqing Jia,Evan Shelhamer,Jeff Donahue,Sergey Karayev,Jonathan Long,Ross Girshick,Sergio Guadarrama,Trevor Darrell +7 more
TL;DR: Caffe as discussed by the authors is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures.
Proceedings ArticleDOI
Caffe: Convolutional Architecture for Fast Feature Embedding
Yangqing Jia,Evan Shelhamer,Jeff Donahue,Sergey Karayev,Jonathan Long,Ross Girshick,Sergio Guadarrama,Trevor Darrell +7 more
TL;DR: Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures.
Proceedings ArticleDOI
Long-term recurrent convolutional networks for visual recognition and description
Jeff Donahue,Lisa Anne Hendricks,Sergio Guadarrama,Marcus Rohrbach,Subhashini Venugopalan,Trevor Darrell,Kate Saenko +6 more
TL;DR: A novel recurrent convolutional architecture suitable for large-scale visual learning which is end-to-end trainable, and shows such models have distinct advantages over state-of-the-art models for recognition or generation which are separately defined and/or optimized.