Intel Corporation

Neelay Pandit

Sherine Abdelhak

Abhishek R Appu

Joydeep Ray

Altug Koker

John Charles Weast

Linda L Hurd

Dukhwan Kim

Narayan Biswal

Stanley J Baran

Gokcen Cilingir

Nilesh V Shah

Barnan Das

Archie Sharma

Mayuresh M Varerkar

Praneetha Kotha

Mike B Macpherson

Mike MacPherson

A graphics processor can include a processing cluster array including a plurality of processing clusters coupled with the plurality of memory controllers, each processing cluster of the plurality of processing clusters including a plurality of streaming multiprocessors, the processing cluster array configured for partitioning into a plurality of partitions. The plurality of partitions include a first partition including a first plurality of streaming multiprocessors configured to perform operations for a first neural network, The operations for the first neural network are isolated to the first partition. The plurality of partitions also include a second partition including a second plurality of streaming multiprocessors configured to perform operations for a second neural network. The operations for the second neural network are isolated to the second partition and protected from operations performed for the first neural network.

Graphics neural network processor, method, and system

Methods, apparatus, systems and articles of manufacture to generate temporal representations for action recognition systems are disclosed. An example apparatus includes an optical flow computer to compute first optical flows for a video based on computing differences between pairs of frames of the video; an evolution of trajectories (EoT) generator to generate EoT temporal representations of the video based on (a) computing second optical flows of the video and (b) aggregating ones of the second optical flows having different time spans; an action identifier to: identify first actions in the video based on the first optical flows; identify second actions in the video based on the EoT temporal representations; and identify third actions in the video based on frames of the video; and a fuser to determine a fourth action based on a weighted average of the first actions, second actions, and third actions.

Methods and apparatus to generate temporal representations for action recognition systems

A mechanism is described for facilitating recognition, reidentification, and security in machine learning at autonomous machines. A method of embodiments, as described herein, includes facilitating a camera to detect one or more objects within a physical vicinity, the one or more objects including a person, and the physical vicinity including a house, where detecting includes capturing one or more images of one or more portions of a body of the person. The method may further include extracting body features based on the one or more portions of the body, comparing the extracted body features with feature vectors stored at a database, and building a classification model based on the extracted body features over a period of time to facilitate recognition or reidentification of the person independent of facial recognition of the person.

Recognition, reidentification and security enhancements using autonomous machines

Methods, apparatus, systems and articles of manufacture to generate temporal representations for action recognition systems are disclosed. An example apparatus includes an optical flow computer to compute a first optical flow based on first and second video frames separated by a first amount of time and compute a second optical flow based on third and fourth video frames separated by a second amount of time, the second amount of time different than the first amount of time, and an aggregator to combine the first optical flow and the second optical flow to form an image representing action in a video.

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Beaverton, OR, United States of America

Neelay Pandit