M
Menglong Zhu
Researcher at Google
Publications - 45
Citations - 40788
Menglong Zhu is an academic researcher from Google. The author has contributed to research in topics: Object detection & Deep learning. The author has an hindex of 25, co-authored 45 publications receiving 25135 citations. Previous affiliations of Menglong Zhu include Mitsubishi Electric Research Laboratories & University of Pennsylvania.
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MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Andrew Howard,Menglong Zhu,Bo Chen,Dmitry Kalenichenko,Weijun Wang,Tobias Weyand,M. Andreetto,Hartwig Adam +7 more
TL;DR: This work introduces two simple global hyper-parameters that efficiently trade off between latency and accuracy and demonstrates the effectiveness of MobileNets across a wide range of applications and use cases including object detection, finegrain classification, face attributes and large scale geo-localization.
Proceedings ArticleDOI
MobileNetV2: Inverted Residuals and Linear Bottlenecks
TL;DR: MobileNetV2 as mentioned in this paper is based on an inverted residual structure where the shortcut connections are between the thin bottleneck layers and intermediate expansion layer uses lightweight depthwise convolutions to filter features as a source of non-linearity.
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MobileNetV2: Inverted Residuals and Linear Bottlenecks
TL;DR: A new mobile architecture, MobileNetV2, is described that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes and allows decoupling of the input/output domains from the expressiveness of the transformation.
Proceedings ArticleDOI
Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors
Jonathan Huang,Vivek Rathod,Chen Sun,Menglong Zhu,Anoop Korattikara,Alireza Fathi,Ian Fischer,Zbigniew Wojna,Yang Song,Sergio Guadarrama,Kevin Murphy +10 more
TL;DR: A unified implementation of the Faster R-CNN, R-FCN and SSD systems is presented and the speed/accuracy trade-off curve created by using alternative feature extractors and varying other critical parameters such as image size within each of these meta-architectures is traced out.
Proceedings ArticleDOI
Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
Benoit Jacob,Skirmantas Kligys,Bo Chen,Menglong Zhu,Matthew Tang,Andrew Howard,Hartwig Adam,Dmitry Kalenichenko +7 more
TL;DR: A quantization scheme is proposed that allows inference to be carried out using integer- only arithmetic, which can be implemented more efficiently than floating point inference on commonly available integer-only hardware.