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[D] Paper review + interview; Learning spatiotemporal [video action] features with 3d convolutional networks

Join Karol Zak on a tour of video action detection using deep learning using 3DCNNs. Karol is going to cover off a breakthrough paper from 2015 “Learning spatiotemporal features with 3d convolutional networks”. 3D convnets are conceptually very easy to use and understand, they work like normal 2D CNNs but they use the third (depth) dimension to capture the time domain. Several large video action datasets have appeared on the scene too which have significantly democratised the practice i.e. Sports1M, Kinetics.

Karol’s style is very practical and hands-on and like last time will demonstrate the models working live in a Jupyter notebook and talk to some of his experience with video action detection.

https://www.youtube.com/watch?v=Ty-gww6GwHY

Paper link; https://arxiv.org/abs/1412.0767

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.