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

Class-agnostic counting methods enumerate objects of an arbitrary class,providing tremendous utility in many fields. Prior works have limitedusefulness as they require either a set of examples of the type to be countedor that the image contains only a single type of object. A significant factorin these shortcomings is the lack of a dataset to properly address counting insettings with more than one kind of object present. To address these issues, wepropose the first Multi-class, Class-Agnostic Counting dataset (MCAC) and ABlind Counter (ABC123), a method that can count multiple types of objectssimultaneously without using examples of type during training or inference.ABC123 introduces a new paradigm where instead of requiring exemplars to guidethe enumeration, examples are found after the counting stage to help a userunderstand the generated outputs. We show that ABC123 outperforms contemporarymethods on MCAC without the requirement of human in-the-loop annotations. Wealso show that this performance transfers to FSC-147, the standardclass-agnostic counting dataset.

ABC Easy as 123: A Blind Counter for Exemplar-Free Multi-Class Class-agnostic Counting