New deep learning model brings image segmentation to edge devices

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New deep learning model brings image segmentation to edge devices

New deep learning model brings image segmentation to edge devices

“A new neural network architecture designed by artificial intelligence researchers at DarwinAI and the University of Waterloo will make it possible to perform image segmentation on computing devices with low-power and -compute capacity.

Segmentation is the process of determining the boundaries and areas of objects in images. We humans perform segmentation without conscious effort, but it remains a key challenge for machine learning systems. It is vital to the functionality of mobile robots, self-driving cars, and other artificial intelligence systems that must interact and navigate the real world.

Until recently, segmentation required large, compute-intensive neural networks. This made it difficult to run these deep learning models without a connection to cloud servers.

In their latest work, the scientists at DarwinAI and the University of Waterloo have managed to create a neural network that provides near-optimal segmentation and is small enough to fit on resource-constrained devices. Called AttendSeg, the neural network is detailed in a paper that has been accepted at this year’s Conference on Computer Vision and Pattern Recognition (CVPR)…”

Source: venturebeat.com/2021/05/14/new-deep-learning-model-brings-image-segmentation-to-edge-devices/

May 21, 2021
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