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The Zane Body Training Manual.

Product details File Size: November 28, Sold by: Share your thoughts with other customers. Write a customer review. Showing of 2 reviews. Top Reviews Most recent Top Reviews. There was a problem filtering reviews right now. Please try again later. Great information with sound advice. I look forward to more readings from Frank and his years of knowledge.

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He received an Hon. He served as Director of the School of Computing Science from Mori conducts research in computer vision and machine learning, and teaches classes in data structures and programming, artificial intelligence, computer vision, and machine learning.

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He is privileged to have worked with many excellent students while at SFU. I have made significant contributions towards solving the problems of human pose estimation and human action recognition. At a broad level, the methodology followed is to construct features and representations that capture our intuition regarding these vision problems. We operationalize these via machine learning algorithms, adapting them to suit our purposes.

Specific examples of features and representations include work on superpixels for representing images, motion features for human action recognition, and our structured models for video sequences and group activities.

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A method for learning latent dependency structures in VAEs is developed. The paper describes a semantic prior for image generation. Ibrahim successfully defended his Ph. A programmatic approach to constructing priors for VAE-based generative models for complex scenes is proposed. A temporal variational auto-encoder for synthesizing controllable sequences of human motion is presented. A deep network compression algorithm for meeting operational performance constraints is presented.

A sparse aggregation approach for deep neural networks is proposed and analyzed.



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An object reasoning approach to video understanding is presented. A relational neural network layer for supervised and unsupervised learning of human-contextual feature learning for group activity recognition is presented. An adaptive rendering approach for generating images of human action is developed.