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/

Zendy Plus

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

Zendy Tools

Zendy Open

We address the problem of segmenting an object given a natural languageexpression that describes it. Current techniques tackle this task by either(\textit{i}) directly or recursively merging linguistic and visual informationin the channel dimension and then performing convolutions; or by (\textit{ii})mapping the expression to a space in which it can be thought of as a filter,whose response is directly related to the presence of the object at a givenspatial coordinate in the image, so that a convolution can be applied to lookfor the object. We propose a novel method that integrates these two insights inorder to fully exploit the recursive nature of language. Additionally, duringthe upsampling process, we take advantage of the intermediate informationgenerated when downsampling the image, so that detailed segmentations can beobtained. We compare our method against the state-of-the-art approaches in fourstandard datasets, in which it surpasses all previous methods in six of eightof the splits for this task.

Dynamic Multimodal Instance Segmentation guided by natural language queries