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

Convolutional Neural Networks (CNNs) are successfully used for the importantautomotive visual perception tasks including object recognition, motion anddepth estimation, visual SLAM, etc. However, these tasks are typicallyindependently explored and modeled. In this paper, we propose a jointmulti-task network design for learning several tasks simultaneously. Our mainmotivation is the computational efficiency achieved by sharing the expensiveinitial convolutional layers between all tasks. Indeed, the main bottleneck inautomated driving systems is the limited processing power available ondeployment hardware. There is also some evidence for other benefits inimproving accuracy for some tasks and easing development effort. It also offersscalability to add more tasks leveraging existing features and achieving bettergeneralization. We survey various CNN based solutions for visual perceptiontasks in automated driving. Then we propose a unified CNN model for theimportant tasks and discuss several advanced optimization and architecturedesign techniques to improve the baseline model. The paper is partly review andpartly positional with demonstration of several preliminary results promisingfor future research. We first demonstrate results of multi-stream learning andauxiliary learning which are important ingredients to scale to a largemulti-task model. Finally, we implement a two-stream three-task network whichperforms better in many cases compared to their corresponding single-taskmodels, while maintaining network size.

NeurAll: Towards a Unified Model for Visual Perception in Automated Driving