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

This paper presents a self-adaptive training (SAT) model for fashioncompatibility prediction. It focuses on the learning of some hard items, suchas those that share similar color, texture, and pattern features but areconsidered incompatible due to the aesthetics or temporal shifts. Specifically,we first design a method to define hard outfits and a difficulty score (DS) isdefined and assigned to each outfit based on the difficulty in recommending anitem for it. Then, we propose a self-adaptive triplet loss (SATL), where the DSof the outfit is considered. Finally, we propose a very simple conditionalsimilarity network combining the proposed SATL to achieve the learning of harditems in the fashion compatibility prediction. Experiments on the publiclyavailable Polyvore Outfits and Polyvore Outfits-D datasets demonstrate ourSAT's effectiveness in fashion compatibility prediction. Besides, our SATL canbe easily extended to other conditional similarity networks to improve theirperformance.

SAT: Self-adaptive training for fashion compatibility prediction