  Our goal is to design architectures that retain the groundbreaking
performance of CNNs for landmark localization and at the same time are
lightweight, compact and suitable for applications with limited computational
resources. To this end, we make the following contributions: (a) we are the
first to study the effect of neural network binarization on localization tasks,
namely human pose estimation and face alignment. We exhaustively evaluate
various design choices, identify performance bottlenecks, and more importantly
propose multiple orthogonal ways to boost performance. (b) Based on our
analysis, we propose a novel hierarchical, parallel and multi-scale residual
architecture that yields large performance improvement over the standard
bottleneck block while having the same number of parameters, thus bridging the
gap between the original network and its binarized counterpart. (c) We perform
a large number of ablation studies that shed light on the properties and the
performance of the proposed block. (d) We present results for experiments on
the most challenging datasets for human pose estimation and face alignment,
reporting in many cases state-of-the-art performance. Code can be downloaded
from https://www.adrianbulat.com/binary-cnn-landmarks
