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Our goal is to design architectures that retain the groundbreakingperformance of CNNs for landmark localization and at the same time arelightweight, compact and suitable for applications with limited computationalresources. To this end, we make the following contributions: (a) we are thefirst to study the effect of neural network binarization on localization tasks,namely human pose estimation and face alignment. We exhaustively evaluatevarious design choices, identify performance bottlenecks, and more importantlypropose multiple orthogonal ways to boost performance. (b) Based on ouranalysis, we propose a novel hierarchical, parallel and multi-scale residualarchitecture that yields large performance improvement over the standardbottleneck block while having the same number of parameters, thus bridging thegap between the original network and its binarized counterpart. (c) We performa large number of ablation studies that shed light on the properties and theperformance of the proposed block. (d) We present results for experiments onthe most challenging datasets for human pose estimation and face alignment,reporting in many cases state-of-the-art performance. Code can be downloadedfrom https://www.adrianbulat.com/binary-cnn-landmarks

Binarized Convolutional Landmark Localizers for Human Pose Estimation and Face Alignment with Limited Resources