Catchoom Briefing Note

Document ID: CGBN125 | Last Updated: May 13, 2018 Abstract Without the power of AI and ML, humans would have to do the work to look at a video or image and determine what the specifics of those images are for a particular purpose. Now, with sufficient training on ML neural net models leveraging technologies …

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Catchoom Briefing Note [CGBN125]

Without the power of AI and ML, humans would have to do the work to look at a video or image and determine what the specifics of those images are for a particular purpose. Now, with sufficient training on ML neural net models leveraging technologies such as Deep Learning, systems can automatically, and autonomously, identify …

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Clarifai Briefing Note (CGBN112)

Computers acquire images through various sensors that turn light and objects into binary data, but computers can’t understand what that data means. For a long time, this didn’t really matter because a computer would just store those images for later consumption by human. However, mere capture of images is no longer sufficient. In order to …

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Clarifai Briefing Note

Document ID: CGBN112 | Last Updated: Feb. 7, 2018 Abstract Computers acquire images through various sensors that turn light and objects into binary data, but computers can’t understand what that data means. For a long time, this didn’t really matter because a computer would just store those images for later consumption by human. However, mere …

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SenseTime Briefing Note [CGBN103]

For a long time, it seemed that getting accurate image recognition was a computationally very difficult and hard task. Developers and researchers found it difficult to be able to program their way to distinguishing one face from another and in different positions, lighting situations, and facial expressions. Then Deep Learning came and changed everything. Into …

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SenseTime Briefing Note

Document ID: CGBN103 | Last Updated: Jan. 18, 2018 Abstract For a long time, it seemed that getting accurate image recognition was a computationally very difficult and hard task. Developers and researchers found it difficult to be able to program their way to distinguishing one face from another and in different positions, lighting situations, and …

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