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3. Take home test - implement a method of Facial landmark localization from a paper called 'Supervised Descent Method and its Applications to Face Alignment' from 2013. Note - the instruction they provide implies it's more of a coding task, but they actually expect you to have a deep understanding of everything: * How the SIFT/HoG descriptors are computed and why? * Why augmentations are needed? * What's better to have and why (in terms of prediction accuracy) - one model to predict all landmark points or a different model for each point.
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Computer Vision Researcher

Interviewed at Lightricks

4★
Jan 13, 2025

3. Take home test - implement a method of Facial landmark localization from a paper called 'Supervised Descent Method and its Applications to Face Alignment' from 2013. Note - the instruction they provide implies it's more of a coding task, but they actually expect you to have a deep understanding of everything: * How the SIFT/HoG descriptors are computed and why? * Why augmentations are needed? * What's better to have and why (in terms of prediction accuracy) - one model to predict all landmark points or a different model for each point.

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