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

The recent advent of self-supervised pre-training techniques has led to asurge in the use of multimodal learning in form document understanding.However, existing approaches that extend the mask language modeling to othermodalities require careful multi-task tuning, complex reconstruction targetdesigns, or additional pre-training data. In FormNetV2, we introduce acentralized multimodal graph contrastive learning strategy to unifyself-supervised pre-training for all modalities in one loss. The graphcontrastive objective maximizes the agreement of multimodal representations,providing a natural interplay for all modalities without special customization.In addition, we extract image features within the bounding box that joins apair of tokens connected by a graph edge, capturing more targeted visual cueswithout loading a sophisticated and separately pre-trained image embedder.FormNetV2 establishes new state-of-the-art performance on FUNSD, CORD, SROIEand Payment benchmarks with a more compact model size.

FormNetV2: Multimodal Graph Contrastive Learning for Form Document Information Extraction