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

In this paper we tackle the problem of instance-level segmentation and depthordering from a single monocular image. Towards this goal, we take advantage ofconvolutional neural nets and train them to directly predict instance-levelsegmentations where the instance ID encodes the depth ordering within imagepatches. To provide a coherent single explanation of an image we develop aMarkov random field which takes as input the predictions of convolutionalneural nets applied at overlapping patches of different resolutions, as well asthe output of a connected component algorithm. It aims to predict accurateinstance-level segmentation and depth ordering. We demonstrate theeffectiveness of our approach on the challenging KITTI benchmark and show goodperformance on both tasks.

Monocular Object Instance Segmentation and Depth Ordering with CNNs