Skip to main content

Research Repository

Advanced Search

Hierarchical binary CNNs for landmark localization with limited resources

Bulat, Adrian; Tzimiropoulos, Georgios


Adrian Bulat

Georgios Tzimiropoulos


Our goal is to design architectures that retain the groundbreaking performance of Convolutional Neural Networks (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. (e) We further provide additional results for the problem of facial part segmentation. Code can be downloaded from


Bulat, A., & Tzimiropoulos, G. (2020). Hierarchical binary CNNs for landmark localization with limited resources. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(2), 343 - 356.

Journal Article Type Article
Acceptance Date Aug 5, 2018
Online Publication Date Aug 23, 2018
Publication Date 2020-02
Deposit Date Aug 16, 2018
Publicly Available Date Sep 11, 2018
Journal IEEE Transactions on Pattern Analysis and Machine Intelligence
Print ISSN 0162-8828
Electronic ISSN 1939-3539
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 42
Issue 2
Pages 343 - 356
Keywords Binary Convolutional Neural Networks; Residual learning; Landmark localization; Human pose estimation; Face alignment.
Public URL
Publisher URL


You might also like

Downloadable Citations