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

Pre-training visual and textual representations from large-scale image-textpairs is becoming a standard approach for many downstream vision-languagetasks. The transformer-based models learn inter and intra-modal attentionthrough a list of self-supervised learning tasks. This paper proposes LAViTeR,a novel architecture for visual and textual representation learning. The mainmodule, Visual Textual Alignment (VTA) will be assisted by two auxiliary tasks,GAN-based image synthesis and Image Captioning. We also propose a newevaluation metric measuring the similarity between the learnt visual andtextual embedding. The experimental results on two public datasets, CUB andMS-COCO, demonstrate superior visual and textual representation alignment inthe joint feature embedding space

LAViTeR: Learning Aligned Visual and Textual Representations Assisted by Image and Caption Generation