🔻 Open Images V6 — Now Featuring Localized Narratives

Open Images is the largest annotated image dataset in many regards, for use in training the latest deep #convolutional #neural_networks for #computer_vision tasks. With the introduction of version 5 last May, the Open Images dataset includes 9M images annotated with 36M image-level labels, 15.8M bounding boxes, 2.8M instance #segmentations, and 391k visual relationships. Along with the dataset itself, the associated Open Images Challenges have spurred the latest advances in #object_detection, instance segmentation, and visual relationship detection.

📌 Via: @cedeeplearning

link: https://ai.googleblog.com/search?updated-max=2020-03-11T09:00:00-07:00&max-results=10

#image_detection
#machinelearning
#deeplearning

🔻Does Deep Learning Come from the Devil?

Deep learning has revolutionized #computer_vision and natural language processing (#NLP). Yet the #mathematics explaining its success remains elusive. At the Yandex conference on machine learning prospects and applications, Vladimir Vapnik offered a critical perspective.

🔹we suggest you to tap the link🔹

link: https://www.kdnuggets.com/2015/10/deep-learning-vapnik-einstein-devil-yandex-conference.html

📌Via: @cedeeplearning

#deeplearning
#machinelearning
#neuralnetworks

🔹Deep learning with point clouds

Research aims to make it easier for #self_driving cars, robotics, and other applications to understand the 3D world.
“In #computer_vision and machine learning today, 90 percent of the advances deal only with two-dimensional images,” says MIT Professor Justin Solomon, who was senior author of the new series of papers spearheaded by PhD student Yue Wang. “Our work aims to address a fundamental need to better represent the 3D world, with application not just in autonomous driving, but any field that requires understanding 3D shapes.”
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link: http://news.mit.edu/2019/deep-learning-point-clouds-1021

📌Via: @cedeeplearning

#deeplearning
#machinelearning
#neuralnetworks

🔹Deep Learning technologies impacting computer vision advances

A significant focus of study in the field of computer vision is on systems to recognize and remove highlights from digital pictures. Extracted features context for inference about an image, and often the more extravagant the highlights, the better the derivation.

Until not long ago, facial recognition was an awkward and costly innovation constrained to police research labs. However, as of late, because of advances in #computer_vision #algorithms, #facial_recognition has discovered its way into different computing gadgets.
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

link: https://www.analyticsinsight.net/deep-learning-technologies-impacting-computer-vision-advances/

#deeplearning
#neuralnetworks
#machinelearning

📕 deep learning in computer vision
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📌Via: @cedeeplearning

#deeplearning #math #AI
#computer_vision #neuralnetworks
#machinelearning #datascience