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 perceptual-based grouping process produces a hierarchical andcompositional image representation that helps both human and machine visionsystems recognize heterogeneous visual concepts. Examples can be found in theclassical hierarchical superpixel segmentation or image parsing works. However,the grouping process is largely overlooked in modern CNN-based imagesegmentation networks due to many challenges, including the inherentincompatibility between the grid-shaped CNN feature map and theirregular-shaped perceptual grouping hierarchy. Overcoming these challenges, wepropose a deep grouping model (DGM) that tightly marries the two types ofrepresentations and defines a bottom-up and a top-down process for featureexchanging. When evaluating the model on the recent Broden+ dataset for theunified perceptual parsing task, it achieves state-of-the-art results whilehaving a small computational overhead compared to other contextual-basedsegmentation models. Furthermore, the DGM has better interpretability comparedwith modern CNN methods.

Deep Grouping Model for Unified Perceptual Parsing