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

Most publicly available datasets for image classification are with singlelabels, while images are inherently multi-labeled in our daily life. Such anannotation gap makes many pre-trained single-label classification models failin practical scenarios. This annotation issue is more concerned for aerialimages: Aerial data collected from sensors naturally cover a relatively largeland area with multiple labels, while annotated aerial datasets, which arepublicly available (e.g., UCM, AID), are single-labeled. As manually annotatingmulti-label aerial images would be time/labor-consuming, we propose a novelself-correction integrated domain adaptation (SCIDA) method for automaticmulti-label learning. SCIDA is weakly supervised, i.e., automatically learningthe multi-label image classification model from using massive, publiclyavailable single-label images. To achieve this goal, we propose a novelLabel-Wise self-Correction (LWC) module to better explore underlying labelcorrelations. This module also makes the unsupervised domain adaptation (UDA)from single- to multi-label data possible. For model training, the proposedmodel only uses single-label information yet requires no prior knowledge ofmulti-labeled data; and it predicts labels for multi-label aerial images. Inour experiments, trained with single-labeled MAI-AID-s and MAI-UCM-s datasets,the proposed model is tested directly on our collected Multi-scene Aerial Image(MAI) dataset.

SCIDA: Self-Correction Integrated Domain Adaptation From Single- to Multi-Label Aerial Images