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

Semantic segmentation has made significant progress in recent years thanks todeep neural networks, but the common objective of generating a singlesegmentation output that accurately matches the image's content may not besuitable for safety-critical domains such as medical diagnostics and autonomousdriving. Instead, multiple possible correct segmentation maps may be requiredto reflect the true distribution of annotation maps. In this context,stochastic semantic segmentation methods must learn to predict conditionaldistributions of labels given the image, but this is challenging due to thetypically multimodal distributions, high-dimensional output spaces, and limitedannotation data. To address these challenges, we propose a conditionalcategorical diffusion model (CCDM) for semantic segmentation based on DenoisingDiffusion Probabilistic Models. Our model is conditioned to the input image,enabling it to generate multiple segmentation label maps that account for thealeatoric uncertainty arising from divergent ground truth annotations. Ourexperimental results show that CCDM achieves state-of-the-art performance onLIDC, a stochastic semantic segmentation dataset, and outperforms establishedbaselines on the classical segmentation dataset Cityscapes.

Stochastic Segmentation with Conditional Categorical Diffusion Models