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

Sign language recognition (SLR) is a challenging problem, involving complexmanual features, i.e., hand gestures, and fine-grained non-manual features(NMFs), i.e., facial expression, mouth shapes, etc. Although manual featuresare dominant, non-manual features also play an important role in the expressionof a sign word. Specifically, many sign words convey different meanings due tonon-manual features, even though they share the same hand gestures. Thisambiguity introduces great challenges in the recognition of sign words. Totackle the above issue, we propose a simple yet effective architecture calledGlobal-local Enhancement Network (GLE-Net), including two mutually promotedstreams towards different crucial aspects of SLR. Of the two streams, onecaptures the global contextual relationship, while the other stream capturesthe discriminative fine-grained cues. Moreover, due to the lack of datasetsexplicitly focusing on this kind of features, we introduce the firstnon-manual-features-aware isolated Chinese sign language dataset~(NMFs-CSL)with a total vocabulary size of 1,067 sign words in daily life. Extensiveexperiments on NMFs-CSL and SLR500 datasets demonstrate the effectiveness ofour method.

Global-local Enhancement Network for NMFs-aware Sign Language Recognition