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Filter results for [machine vision]
22/09/20

source: interview between Kay Watson and Rebecca Allen (Serpentine...

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Lacework

June 2020

Lacework is a new work by Everest Pipkin that uses artificial neural networks to reinscribe the videos of MIT’s Moments in Time Dataset. Using algorithms that stretch time and add details to images, Pipkin creates a series of hallucinatory slow-motion vignettes from the videos of everyday actions that form the collection. By manipulating the source videos of the MIT dataset, Lacework presents a river of these moments, as captured in amber; flowing from one to another into a cascade of gradual, unfolding details.

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16/04/20

The face of your voice 3D, from the verbal to the physiognomic Contemporary life seems to be an endless game of data...

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04/03/20

Given that a person’s gender cannot be inferred by appearance, we have decided to remove these labels in order to align with the...

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I’m looking at you, looking at me

November 2019

In Heather Dewey-Hagborg’s artwork ‘How do you see me?’, commissioned for the Data/Set/Match programme at The Photographers’ Gallery, the artist explores how machines see us. A question that has been carefully slipping through several areas of production and research during the past couple of decades. At the same time an essential need has also emerged to understand the processes and internal mechanisms that are usually hidden from or mysterious to the user: commenting on those who code, train, build these mechanisms and how this translates into what happens outside of the screen.

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27/09/19

What do you see, YOLO9000? by Taller Estampa | Soy Cámara YOLO9000 is a trained object recognition neuronal network with a...

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27/08/19

The success of ImageNet highlighted that in the era of deep learning, data was at least as important as algorithms. Not only did...

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16/07/19

High Quality Face Recognition with Deep Metric Learning The new example comes with pictures of bald Hollywood action heroes and...

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14/05/19

Let’s play  Name That Dataset!!! https://people.csail.mit.edu/torralba/research/bias/

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