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

Visual similarities discovery (VSD) is an important task with broade-commerce applications. Given an image of a certain object, the goal of VSD isto retrieve images of different objects with high perceptual visual similarity.Although being a highly addressed problem, the evaluation of proposed methodsfor VSD is often based on a proxy of an identification-retrieval task,evaluating the ability of a model to retrieve different images of the sameobject. We posit that evaluating VSD methods based on identification tasks islimited, and faithful evaluation must rely on expert annotations. In thispaper, we introduce the first large-scale fashion visual similarity benchmarkdataset, consisting of more than 110K expert-annotated image pairs. Besidesthis major contribution, we share insight from the challenges we faced whilecurating this dataset. Based on these insights, we propose a novel andefficient labeling procedure that can be applied to any dataset. Our analysisexamines its limitations and inductive biases, and based on these findings, wepropose metrics to mitigate those limitations. Though our primary focus lies onvisual similarity, the methodologies we present have broader applications fordiscovering and evaluating perceptual similarity across various domains.

Efficient Discovery and Effective Evaluation of Visual Perceptual Similarity: A Benchmark and Beyond