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 semantic segmentation of parts of objects in the wild is a challengingtask in which multiple instances of objects and multiple parts within thoseobjects must be detected in the scene. This problem remains nowadays verymarginally explored, despite its fundamental importance towards detailed objectunderstanding. In this work, we propose a novel framework combining higherobject-level context conditioning and part-level spatial relationships toaddress the task. To tackle object-level ambiguity, a class-conditioning moduleis introduced to retain class-level semantics when learning parts-levelsemantics. In this way, mid-level features carry also this information prior tothe decoding stage. To tackle part-level ambiguity and localization we proposea novel adjacency graph-based module that aims at matching the relative spatialrelationships between ground truth and predicted parts. The experimentalevaluation on the Pascal-Part dataset shows that we achieve state-of-the-artresults on this task.

GMNet: Graph Matching Network for Large Scale Part Semantic Segmentation in the Wild