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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The Non-Local Network (NLNet) presents a pioneering approach for capturinglong-range dependencies, via aggregating query-specific global context to eachquery position. However, through a rigorous empirical analysis, we have foundthat the global contexts modeled by non-local network are almost the same fordifferent query positions within an image. In this paper, we take advantage ofthis finding to create a simplified network based on a query-independentformulation, which maintains the accuracy of NLNet but with significantly lesscomputation. We further observe that this simplified design shares similarstructure with Squeeze-Excitation Network (SENet). Hence we unify them into athree-step general framework for global context modeling. Within the generalframework, we design a better instantiation, called the global context (GC)block, which is lightweight and can effectively model the global context. Thelightweight property allows us to apply it for multiple layers in a backbonenetwork to construct a global context network (GCNet), which generallyoutperforms both simplified NLNet and SENet on major benchmarks for variousrecognition tasks. The code and configurations are released athttps://github.com/xvjiarui/GCNet.

GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond