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

Autonomous robots deal with unexpected scenarios in real environments. Giveninput images, various visual perception tasks can be performed, e.g., semanticsegmentation, depth estimation and normal estimation. These different tasksprovide rich information for the whole robotic perception system. All taskshave their own characteristics while sharing some latent correlations. However,some of the task predictions may suffer from the unreliability dealing withcomplex scenes and anomalies. We propose an attention-based failure detectionapproach by exploiting the correlations among multiple tasks. The proposedframework infers task failures by evaluating the individual prediction, acrossmultiple visual perception tasks for different regions in an image. Theformulation of the evaluations is based on an attention network supervised bymulti-task uncertainty estimation and their corresponding prediction errors.Our proposed framework generates more accurate estimations of the predictionerror for the different task's predictions.

See Yourself in Others: Attending Multiple Tasks for Own Failure Detection