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

Recently, anchor-based methods have achieved great progress in facedetection. Once anchor design and anchor matching strategy determined, plentyof positive anchors will be sampled. However, faces with extreme aspect ratioalways fail to be sampled according to standard anchor matching strategy. Infact, the max IoUs between anchors and extreme aspect ratio faces are stilllower than fixed sampling threshold. In this paper, we firstly explore thefactors that affect the max IoU of each face in theory. Then, anchor matchingsimulation is performed to evaluate the sampling range of face aspect ratio.Besides, we propose a Wide Aspect Ratio Matching (WARM) strategy to collectmore representative positive anchors from ground-truth faces across a widerange of aspect ratio. Finally, we present a novel feature enhancement module,named Receptive Field Diversity (RFD) module, to provide diverse receptivefield corresponding to different aspect ratios. Extensive experiments show thatour method can help detectors better capture extreme aspect ratio faces andachieve promising detection performance on challenging face detectionbenchmarks, including WIDER FACE and FDDB datasets.

Wide Aspect Ratio Matching for Robust Face Detection