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

We study the impact of using rich and diverse textual descriptions of classesfor zero-shot learning (ZSL) on ImageNet. We create a new dataset ImageNet-Wikithat matches each ImageNet class to its corresponding Wikipedia article. Weshow that merely employing these Wikipedia articles as class descriptionsyields much higher ZSL performance than prior works. Even a simple model usingthis type of auxiliary data outperforms state-of-the-art models that rely onstandard features of word embedding encodings of class names. These resultshighlight the usefulness and importance of textual descriptions for ZSL, aswell as the relative importance of auxiliary data type compared to algorithmicprogress. Our experimental results also show that standard zero-shot learningapproaches generalize poorly across categories of classes.

Large-Scale Zero-Shot Image Classification from Rich and Diverse Textual Descriptions