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 present ViP-DeepLab, a unified model attempting to tacklethe long-standing and challenging inverse projection problem in vision, whichwe model as restoring the point clouds from perspective image sequences whileproviding each point with instance-level semantic interpretations. Solving thisproblem requires the vision models to predict the spatial location, semanticclass, and temporally consistent instance label for each 3D point. ViP-DeepLabapproaches it by jointly performing monocular depth estimation and videopanoptic segmentation. We name this joint task as Depth-aware Video PanopticSegmentation, and propose a new evaluation metric along with two deriveddatasets for it, which will be made available to the public. On the individualsub-tasks, ViP-DeepLab also achieves state-of-the-art results, outperformingprevious methods by 5.1% VPQ on Cityscapes-VPS, ranking 1st on the KITTImonocular depth estimation benchmark, and 1st on KITTI MOTS pedestrian. Thedatasets and the evaluation codes are made publicly available.

ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation