J
Joseph Redmon
Researcher at University of Washington
Publications - 16
Citations - 64742
Joseph Redmon is an academic researcher from University of Washington. The author has contributed to research in topics: Convolutional neural network & Object detection. The author has an hindex of 14, co-authored 16 publications receiving 40409 citations. Previous affiliations of Joseph Redmon include Google.
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XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
TL;DR: XNOR-Nets as discussed by the authors approximate convolutions using primarily binary operations, which results in 58x faster convolutional operations and 32x memory savings, and outperforms BinaryConnect and BinaryNets by large margins on ImageNet.
Proceedings ArticleDOI
Real-time grasp detection using convolutional neural networks
Joseph Redmon,Anelia Angelova +1 more
TL;DR: An accurate, real-time approach to robotic grasp detection based on convolutional neural networks that outperforms state-of-the-art approaches by 14 percentage points and runs at 13 frames per second on a GPU.
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You Only Look Once: Unified, Real-Time Object Detection
TL;DR: YOLO as discussed by the authors predicts bounding boxes and class probabilities directly from full images in one evaluation, which can be optimized end-to-end directly on detection performance, and achieves state-of-the-art performance.
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Real-Time Grasp Detection Using Convolutional Neural Networks
Joseph Redmon,Anelia Angelova +1 more
TL;DR: In this paper, a convolutional neural network (CNN) is used for robotic grasp detection, which performs single-stage regression to graspable bounding boxes without using standard sliding window or region proposal techniques.
Proceedings ArticleDOI
IQA: Visual Question Answering in Interactive Environments
TL;DR: In this paper, a Hierarchical Interactive Memory Network (HIMN) is proposed to operate at multiple levels of temporal abstraction, allowing the agent to interact with a dynamic visual environment.