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

We present a novel approach for unsupervised road segmentation in adverseweather conditions such as rain or fog. This includes a new algorithm forsource-free domain adaptation (SFDA) using self-supervised learning. Moreover,our approach uses several techniques to address various challenges in SFDA andimprove performance, including online generation of pseudo-labels andself-attention as well as use of curriculum learning, entropy minimization andmodel distillation. We have evaluated the performance on $6$ datasetscorresponding to real and synthetic adverse weather conditions. Our methodoutperforms all prior works on unsupervised road segmentation and SFDA by atleast 10.26%, and improves the training time by 18-180x. Moreover, ourself-supervised algorithm exhibits similar accuracy performance in terms ofmIOU score as compared to prior supervised methods.

SS-SFDA : Self-Supervised Source-Free Domain Adaptation for Road Segmentation in Hazardous Environments