English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper addresses 2 challenging tasks: improving the quality of lowresolution facial images and accurately locating the facial landmarks on suchpoor resolution images. To this end, we make the following 5 contributions: (a)we propose Super-FAN: the very first end-to-end system that addresses bothtasks simultaneously, i.e. both improves face resolution and detects the faciallandmarks. The novelty or Super-FAN lies in incorporating structuralinformation in a GAN-based super-resolution algorithm via integrating asub-network for face alignment through heatmap regression and optimizing anovel heatmap loss. (b) We illustrate the benefit of training the two networksjointly by reporting good results not only on frontal images (as in prior work)but on the whole spectrum of facial poses, and not only on synthetic lowresolution images (as in prior work) but also on real-world images. (c) Weimprove upon the state-of-the-art in face super-resolution by proposing a newresidual-based architecture. (d) Quantitatively, we show large improvement overthe state-of-the-art for both face super-resolution and alignment. (e)Qualitatively, we show for the first time good results on real-world lowresolution images.

Super-FAN: Integrated facial landmark localization and super-resolution of real-world low resolution faces in arbitrary poses with GANs