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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

At present, occlusion and appearance similarity pose severe challenges to person re-identification tasks. Although many robust deep convolutional neural networks alleviate these problems, convolutional layers with limited receptive fields cannot model global semantic information well. In addition, in the person re-identification model, many metric losses ignore or destroy the intra-class structure of the sample, which makes the model difficult to be optimized. Therefore, we design a discriminative Re-identification model with global-local attention and adaptive weighted rank list loss (GLWR). Specifically, our global-local attention (GL-Attention) learns the semantic context in the channel and spatial dimensions. By learning the dependencies between features, GL-Attention integrates global semantic information into local features to extract discriminative features. Unlike rank list loss, our adaptive weighted rank list loss (WRLL) adaptively assigns weights according to the metric distance between the negative sample and the input image, which further improves the performance of the model. Experimental studies on three public datasets (Market-1501, DukeMTMC-ReID and CUHK03) indicate that the performance of our GLWR is significantly superior to many of the latest algorithms.

A Discriminative Person Re-Identification Model With Global-Local Attention and Adaptive Weighted Rank List Loss