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On 7 October 2019 the US Commerce Department added 28 Chinese organisations, including facial-recognition and AI firms such as Hikvision, Dahua, iFlytek, Megvii and SenseTime, to its Entity List, restricting their ability to buy US products.
PyTorch was publicly released in January 2017 as a beta, with first-class Python integration and an imperative style. Built by people from the Lua Torch community with Facebook's backing, it later became a widely used deep-learning framework.
On 13 October 2016 Alibaba Cloud announced a Hangzhou project, led by the municipal government, that used its ET AI programme and big-data analytics to predict traffic in real time and adjust traffic lights.
At CVPR 2016 in Las Vegas, the Best Paper Award went to "Deep Residual Learning for Image Recognition" by Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun. Google Scholar later reported it as the most cited research paper over five years.
The 2015 ImageNet classification contest was won by ResNet, a network up to 152 layers deep whose layers learn residual functions relative to their inputs; it reached 3.57% error on the test set and, by one account, exceeded human performance.
On 9 November 2015 Google released TensorFlow, its second-generation machine learning system, as open source under the Apache 2.0 licence, letting people outside Google build and train models with the same system Google used in its own products.
At ILSVRC 2014, GoogLeNet won the classification track with 6.67% top-5 error, while VGG's network won localisation and took second in classification; more than 50 institutions took part.
In 2013 the ILSVRC classification task was won by Clarifai with an ensemble of convolutional networks at 11.7% top-5 error, while OverFeat won localisation. Zeiler and Fergus's visualization paper, also reporting ImageNet results, appeared the same month.
In October 2012 AlexNet, a deep neural network built by a University of Toronto team, won the ImageNet image-classification challenge by a wide margin. Its result showed that large neural networks trained on GPUs could outperform hand-designed vision methods.
A Google Research team trained a 9-layer sparse autoencoder on 10 million unlabeled images, reported a face detector learned without face labels, and reached 15.8% accuracy on 20,000 ImageNet categories.
In July 2011 Google acquired Pittsburgh Pattern Recognition (PittPatt), a Carnegie Mellon spin-off whose software could find and track faces in photos and video, bringing facial-recognition technology into Google.
On 16 June 2011 Microsoft released a free beta of the Kinect for Windows SDK from Microsoft Research, giving developers depth, skeletal-tracking and microphone-array data on Windows 7.
The 2011 ILSVRC saw XRCE, the 2010 runner-up, win classification with compressed high-dimensional signatures and linear SVMs, while the new single-object localization task was won by UvA's selective search approach.
Kinect was Microsoft's motion-sensing, controller-free input device for the Xbox 360, released in November 2010. It used cameras and infrared depth sensing for gesture and skeletal tracking, and sold more than 8 million units in its first 60 days.
In October 2010 Google announced on its corporate blog that it had developed technology for cars that drive themselves, a project led by Sebastian Thrun and framed around safety, time and emissions.
In October 2010 Google said its self-driving cars had driven more than 140,000 miles on public roads, and reported that the only crash was a car rear-ended at a traffic light.
The spring 2010 release of ImageNet, announced by Fei-Fei Li on 11 May 2010, totalled more than 11 million images in 15,500 categories and added bounding boxes, SIFT features and a MATLAB toolbox, alongside a call to join ILSVRC2010.
On 13 April 2010, Fei-Fei Li announced the first ImageNet Large Scale Visual Recognition Challenge, a 1,000-category image categorization benchmark. It ran for eight editions, from 2010 to 2017.
At ICDAR 2009, Alex Graves won three connected handwriting competitions — French, Farsi and Arabic — with LSTM networks trained using Connectionist Temporal Classification, the first time a recurrent neural network won an international contest.
CUDA is NVIDIA's software platform for running general-purpose programs on GPU cores. CUDA Toolkit 1.0 shipped in June 2007, turning graphics chips into parallel processors for science and later for deep learning.
The Conference on Computer Vision and Pattern Recognition (CVPR) held its first meeting in Washington, DC in 1983, organised by Takeo Kanade and Dana H. Ballard. It became one of the central venues where computer vision research is presented and compared.
