MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
- Andrew G. Howard, Menglong Zhu, Hartwig Adam
- Computer ScienceArXiv
- 17 April 2017
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.
FaceNet: A unified embedding for face recognition and clustering
- Florian Schroff, Dmitry Kalenichenko, James Philbin
- Computer ScienceComputer Vision and Pattern Recognition
- 12 March 2015
A system that directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure offace similarity, and achieves state-of-the-art face recognition performance using only 128-bytes perface.
Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
- Benoit Jacob, S. Kligys, Dmitry Kalenichenko
- Computer ScienceIEEE/CVF Conference on Computer Vision and…
- 15 December 2017
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.
MnasFPN: Learning Latency-Aware Pyramid Architecture for Object Detection on Mobile Devices
- Bo Chen, Golnaz Ghiasi, Quoc V. Le
- Computer ScienceComputer Vision and Pattern Recognition
- 2 December 2019
This work proposes MnasFPN, a mobile-friendly search space for the detection head, and combines it with latency-aware architecture search to produce efficient object detection models.
Looking Fast and Slow: Memory-Guided Mobile Video Object Detection
- Mason Liu, Menglong Zhu, Marie White, Yinxiao Li, Dmitry Kalenichenko
- Computer ScienceArXiv
- 25 March 2019
This paper addresses the analogous question of whether using memory in computer vision systems can not only improve the accuracy of object detection in video streams, but also reduce the computation time by interleaving conventional feature extractors with extremely lightweight ones which only need to recognize the gist of the scene.
[Re] Explaining in Style: Training a GAN to explain a classifier in StyleSpace
- Sherjil Ozair, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand
- Computer Science
- 2022
StylEx is domain and classifier-agnostic, while its explanations are claimed to be human5 interpretable, distinct, coherent and sufficient to produce flipped classifier decisions.
Supplementary Materials for AQD: Towards Accurate Quantized Object Detection
From the results, the proposed method outperforms other methods by a large margin, and achieves 2.7% and 3.5% higher Top-1 accuracy for 4-bit MobileNetV1 and mobileNetV2.
O-Nect: Open Source Interface for Motion Capture using RGB Camera
- Zhe Cao, Shih-En Wei, Hartwig AdamMobileNets
- Computer Science
- 2019
Complex movement animations and realistic physical interactions can be easily recreated using this approach with minimal hardware investment.