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/

Zendy Plus

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

Zendy Tools

Zendy Open

We present a novel region based active learning method for semantic imagesegmentation, called MetaBox+. For acquisition, we train a meta regressionmodel to estimate the segment-wise Intersection over Union (IoU) of eachpredicted segment of unlabeled images. This can be understood as an estimationof segment-wise prediction quality. Queried regions are supposed to minimize tocompeting targets, i.e., low predicted IoU values / segmentation quality andlow estimated annotation costs. For estimating the latter we propose a simplebut practical method for annotation cost estimation. We compare our method toentropy based methods, where we consider the entropy as uncertainty of theprediction. The comparison and analysis of the results provide insights intoannotation costs as well as robustness and variance of the methods. Numericalexperiments conducted with two different networks on the Cityscapes datasetclearly demonstrate a reduction of annotation effort compared to randomacquisition. Noteworthily, we achieve 95%of the mean Intersection over Union(mIoU), using MetaBox+ compared to when training with the full dataset, withonly 10.47% / 32.01% annotation effort for the two networks, respectively.

MetaBox+: A new Region Based Active Learning Method for Semantic Segmentation using Priority Maps