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

Compositional Zero-Shot Learning (CZSL) has emerged as an essential paradigmin machine learning, aiming to overcome the constraints of traditionalzero-shot learning by incorporating compositional thinking into itsmethodology. Conventional zero-shot learning has difficulty managing unfamiliarcombinations of seen and unseen classes because it depends on pre-defined classembeddings. In contrast, Compositional Zero-Shot Learning uses the inherenthierarchies and structural connections among classes, creating new classrepresentations by combining attributes, components, or other semanticelements. In our paper, we propose a novel framework that for the first timecombines the Modern Hopfield Network with a Mixture of Experts (HOMOE) toclassify the compositions of previously unseen objects. Specifically, theModern Hopfield Network creates a memory that stores label prototypes andidentifies relevant labels for a given input image. Following this, the Mixtureof Expert models integrates the image with the fitting prototype to produce thefinal composition classification. Our approach achieves SOTA performance onseveral benchmarks, including MIT-States and UT-Zappos. We also examine howeach component contributes to improved generalization.

HOMOE: A Memory-Based and Composition-Aware Framework for Zero-Shot Learning with Hopfield Network and Soft Mixture of Experts