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

Human vision greatly benefits from the information about sizes of objects.The role of size in several visual reasoning tasks has been thoroughly exploredin human perception and cognition. However, the impact of the information aboutsizes of objects is yet to be determined in AI. We postulate that this ismainly attributed to the lack of a comprehensive repository of sizeinformation. In this paper, we introduce a method to automatically infer objectsizes, leveraging visual and textual information from web. By maximizing thejoint likelihood of textual and visual observations, our method learns reliablerelative size estimates, with no explicit human supervision. We introduce therelative size dataset and show that our method outperforms competitive textualand visual baselines in reasoning about size comparisons.

Are Elephants Bigger than Butterflies? Reasoning about Sizes of Objects