Étiquette : visual recognition

5 juin 2018 /

“Le logo de SenseTime est rarement visible, même en Chine, où l’entreprise est pourtant la plus active. Sa technologie se trouve cependant derrière nombre de logiciels et d’applications populaires. Suning, un vendeur de produits électroménagers chinois à succès, a lancé l’année dernière des magasins autonomes, sans vendeurs. Le paiement par reconnaissance faciale y est possible grâce à SenseTime. Le spécialiste de l’IA aide aussi l’opérateur du métro de Shanghaï à analyser les flux de passagers.Mais le chiffre d’affaires de SenseTime provient essentiellement d’applications moins impressionnantes. L’entreprise fournit la technologie de reconnaissance faciale des principaux constructeurs chinois de smartphones – Huawei, Oppo, Xiaomi et Vivo”.

Source : SenseTime, la start-up chinoise en pointe dans la reconnaissance faciale

12 janvier 2018 /

«Over a 10 week period the scientists showed images to human test subjects and recorded their brainwaves. At times the subjects brains were monitored in real-time while they were looking at the images, other times they were asked to “recall” the images. The researchers used the brain scans to train a deep learning network to “decode” the data and visualize what the person was thinking about».

Source : You think it and a robot sees it: The future is here with mind-reading AI

27 août 2017 /

« Dorénavant, il est encore plus simple pour un diffuseur de contenu de flouter ce qu’il souhaite. L’outil de détection des visages ayant été amélioré pour l’occasion, il est maintenant capable de reconnaître une même personne tout le long d’une vidéo. Ainsi, pour flouter le visage de ces personnes, il vous suffira de cliquer sur la vignette de son visage au sein de l’outil Retouches, et Blur Faces s’occupe de flouter chaque image de la vidéo où le visage est détecté ».

Source : YouTube améliore son outil de floutage des visages dans les vidéos – Tech – Numerama

3 janvier 2017 /

Here’s how it would work: Passengers would step up to the kiosk and be asked a series of questions such as, « Do you have fruits or vegetables in your luggage? » or « Are you carrying any weapons with you? » Eye-detection software and motion and pressure sensors would monitor the passengers as they answer the questions, looking for tell-tale physiological signs of lying or discomfort. The kiosk would also ask a series of innocuous questions to establish baseline measurements so people are just nervous about flying, for example, wouldn’t be unduly singled out.

Source : The lie-detecting security kiosk of the future

14 décembre 2016 /

Machine Learning (ML) is a fast-moving, competitive field. As important as good algorithms are to ML, the state-of-the-art algorithms are reliably available. Compute clusters are also an important ingredient, and they too are easy to access (especially using services like Amazon EC2 and Amazon EC2 Elastic GPUs). What isn’t reliably available are large-scale high quality data sets. The things you need to train your classifiers. That’s where MTurk comes into play.

Source : re:Invent 2016 recap — Machine Learning with Amazon Mechanical Turk

California gives Nvidia the go-ahead to test self-driving cars on public roads

14 décembre 2016 /

Nvidia announced that it was partnering with Chinese web giant Baidu to build a platform for semiautonomous cars. (Baidu has approval to test autonomous cars in California as well.) Nvidia also built test cars, and was training them in parking lots and private roads prior to receiving this new approval from the California DMV. And this summer, a self-driving race car competition called Roborace announced that it was using the Drive PX2 in its vehicles.California has been a hotbed for autonomous testing, but that status is becoming decreasingly unique.

Source : California gives Nvidia the go-ahead to test self-driving cars on public roads – The Verge

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