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

Fashion products typically feature in compositions of a variety of styles atdifferent clothing parts. In order to distinguish images of different fashionproducts, we need to extract both appearance (i.e., "how to describe") andlocalization (i.e.,"where to look") information, and their interactions. Tothis end, we propose a biologically inspired framework for image-based fashionproduct retrieval, which mimics the hypothesized twostream visual processingsystem of human brain. The proposed attentional heterogeneous bilinear network(AHBN) consists of two branches: a deep CNN branch to extract fine-grainedappearance attributes and a fully convolutional branch to extract landmarklocalization information. A joint channel-wise attention mechanism is furtherapplied to the extracted heterogeneous features to focus on important channels,followed by a compact bilinear pooling layer to model the interaction of thetwo streams. Our proposed framework achieves satisfactory performance on threeimage-based fashion product retrieval benchmarks.

Where to Look and How to Describe: Fashion Image Retrieval with an Attentional Heterogeneous Bilinear Network