This paper presents a new mechanism to facilitate the training of mask transformers for efficient panoptic segmentation, democratizing its deployment.
Weakly supervised object localization (WSOL) aims to learn representations that encode object location using only image-level category labels.
We present a next-generation neural network architecture, MOSAIC, for efficient and accurate semantic image segmentation on mobile devices.
When interacting with objects through cameras, or pictures, users often have a specific intent.
With early termination, the average cost can be further reduced to 198M MAdds while maintaining accuracy of 80. 0% on ImageNet.
Ranked #621 on Image Classification on ImageNet
In this paper, we explore scenarios for the excitation of the eccentricity of the planet in binary systems such as this, considering planet-planet scattering, Lidov-Kozai cycles from the binary acting on a single-planet system, or Lidov-Kozai cycles acting on a two-planet system that also undergoes scattering.
Earth and Planetary Astrophysics
In this paper we address the question: can task-specific detectors be trained and represented as a shared set of weights, plus a very small set of additional weights for each task?
Knowledge distillation is one of the most popular and effective techniques for knowledge transfer, model compression and semi-supervised learning.
1 code implementation • 28 Oct 2020 • Steve Bryson, Michelle Kunimoto, Ravi K. Kopparapu, Jeffrey L. Coughlin, William J. Borucki, David Koch, Victor Silva Aguirre, Christopher Allen, Geert Barentsen, Natalie. M. Batalha, Travis Berger, Alan Boss, Lars A. Buchhave, Christopher J. Burke, Douglas A. Caldwell, Jennifer R. Campbell, Joseph Catanzarite, Hema Chandrasekharan, William J. Chaplin, Jessie L. Christiansen, Jorgen Christensen-Dalsgaard, David R. Ciardi, Bruce D. Clarke, William D. Cochran, Jessie L. Dotson, Laurance R. Doyle, Eduardo Seperuelo Duarte, Edward W. Dunham, Andrea K. Dupree, Michael Endl, James L. Fanson, Eric B. Ford, Maura Fujieh, Thomas N. Gautier III, John C. Geary, Ronald L Gilliland, Forrest R. Girouard, Alan Gould, Michael R. Haas, Christopher E. Henze, Matthew J. Holman, Andrew Howard, Steve B. Howell, Daniel Huber, Roger C. Hunter, Jon M. Jenkins, Hans Kjeldsen, Jeffery Kolodziejczak, Kipp Larson, David W. Latham, Jie Li, Savita Mathur, Soren Meibom, Chris Middour, Robert L. Morris, Timothy D. Morton, Fergal Mullally, Susan E. Mullally, David Pletcher, Andrej Prsa, Samuel N. Quinn, Elisa V. Quintana, Darin Ragozzine, Solange V. Ramirez, Dwight T. Sanderfer, Dimitar Sasselov, Shawn E. Seader, Megan Shabram, Avi Shporer, Jeffrey C. Smith, Jason H. Steffen, Martin Still, Guillermo Torres, John Troeltzsch, Joseph D. Twicken, Akm Kamal Uddin, Jeffrey E. Van Cleve, Janice Voss, Lauren Weiss, William F. Welsh, Bill Wohler, Khadeejah A Zamudio
We present occurrence rates for rocky planets in the habitable zones (HZ) of main-sequence dwarf stars based on the Kepler DR25 planet candidate catalog and Gaia-based stellar properties.
Earth and Planetary Astrophysics Solar and Stellar Astrophysics
no code implementations • 10 Oct 2020 • Qifei Wang, Junjie Ke, Joshua Greaves, Grace Chu, Gabriel Bender, Luciano Sbaiz, Alec Go, Andrew Howard, Feng Yang, Ming-Hsuan Yang, Jeff Gilbert, Peyman Milanfar
This approach effectively reduces the total number of parameters and FLOPS, encouraging positive knowledge transfer while mitigating negative interference across domains.
Hardware-aware neural architecture designs have been predominantly focusing on optimizing model performance on single hardware and model development complexity, where another important factor, model deployment complexity, has been largely ignored.
We explore the question of how the resolution of the input image ("input resolution") affects the performance of a neural network when compared to the resolution of the hidden layers ("internal resolution").
To facilitate the development of microcontroller friendly models, we present a new dataset, Visual Wake Words, that represents a common microcontroller vision use-case of identifying whether a person is present in the image or not, and provides a realistic benchmark for tiny vision models.
By leveraging geolocation information we improve top-1 accuracy in iNaturalist from 70. 1% to 79. 0% for a strong baseline image-only model.
We achieve new state of the art results for mobile classification, detection and segmentation.
Ranked #4 on Dichotomous Image Segmentation on DIS-TE3
We introduce a novel method that enables parameter-efficient transfer and multitask learning.
no code implementations • 15 Apr 2019 • Sergei Alyamkin, Matthew Ardi, Alexander C. Berg, Achille Brighton, Bo Chen, Yiran Chen, Hsin-Pai Cheng, Zichen Fan, Chen Feng, Bo Fu, Kent Gauen, Abhinav Goel, Alexander Goncharenko, Xuyang Guo, Soonhoi Ha, Andrew Howard, Xiao Hu, Yuanjun Huang, Donghyun Kang, Jaeyoun Kim, Jong Gook Ko, Alexander Kondratyev, Junhyeok Lee, Seungjae Lee, Suwoong Lee, Zichao Li, Zhiyu Liang, Juzheng Liu, Xin Liu, Yang Lu, Yung-Hsiang Lu, Deeptanshu Malik, Hong Hanh Nguyen, Eunbyung Park, Denis Repin, Liang Shen, Tao Sheng, Fei Sun, David Svitov, George K. Thiruvathukal, Baiwu Zhang, Jingchi Zhang, Xiaopeng Zhang, Shaojie Zhuo
In addition to mobile phones, many autonomous systems rely on visual data for making decisions and some of these systems have limited energy (such as unmanned aerial vehicles also called drones and mobile robots).
We introduce a novel method that enables parameter-efficient transfer and multi-task learning with deep neural networks.
no code implementations • 3 Oct 2018 • Sergei Alyamkin, Matthew Ardi, Achille Brighton, Alexander C. Berg, Yiran Chen, Hsin-Pai Cheng, Bo Chen, Zichen Fan, Chen Feng, Bo Fu, Kent Gauen, Jongkook Go, Alexander Goncharenko, Xuyang Guo, Hong Hanh Nguyen, Andrew Howard, Yuanjun Huang, Donghyun Kang, Jaeyoun Kim, Alexander Kondratyev, Seungjae Lee, Suwoong Lee, Junhyeok Lee, Zhiyu Liang, Xin Liu, Juzheng Liu, Zichao Li, Yang Lu, Yung-Hsiang Lu, Deeptanshu Malik, Eunbyung Park, Denis Repin, Tao Sheng, Liang Shen, Fei Sun, David Svitov, George K. Thiruvathukal, Baiwu Zhang, Jingchi Zhang, Xiaopeng Zhang, Shaojie Zhuo
The Low-Power Image Recognition Challenge (LPIRC, https://rebootingcomputing. ieee. org/lpirc) is an annual competition started in 2015.
In this paper, we propose an automated mobile neural architecture search (MNAS) approach, which explicitly incorporate model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency.
Ranked #786 on Image Classification on ImageNet
We propose a measure to estimate domain similarity via Earth Mover's Distance and demonstrate that transfer learning benefits from pre-training on a source domain that is similar to the target domain by this measure.
Ranked #27 on Fine-Grained Image Classification on CUB-200-2011
This work proposes an algorithm, called NetAdapt, that automatically adapts a pre-trained deep neural network to a mobile platform given a resource budget.
In this paper we describe a new mobile architecture, MobileNetV2, 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.
Ranked #7 on Retinal OCT Disease Classification on OCT2017
The rising popularity of intelligent mobile devices and the daunting computational cost of deep learning-based models call for efficient and accurate on-device inference schemes.
Current approaches for fine-grained recognition do the following: First, recruit experts to annotate a dataset of images, optionally also collecting more structured data in the form of part annotations and bounding boxes.
Ranked #4 on Fine-Grained Image Classification on CUB-200-2011 (using extra training data)