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

360$^\circ$ video saliency detection is one of the challenging benchmarks for360$^\circ$ video understanding since non-negligible distortion anddiscontinuity occur in the projection of any format of 360$^\circ$ videos, andcapture-worthy viewpoint in the omnidirectional sphere is ambiguous by nature.We present a new framework named Panoramic Vision Transformer (PAVER). Wedesign the encoder using Vision Transformer with deformable convolution, whichenables us not only to plug pretrained models from normal videos into ourarchitecture without additional modules or finetuning but also to performgeometric approximation only once, unlike previous deep CNN-based approaches.Thanks to its powerful encoder, PAVER can learn the saliency from three simplerelative relations among local patch features, outperforming state-of-the-artmodels for the Wild360 benchmark by large margins without supervision orauxiliary information like class activation. We demonstrate the utility of oursaliency prediction model with the omnidirectional video quality assessmenttask in VQA-ODV, where we consistently improve performance without any form ofsupervision, including head movement.

Panoramic Vision Transformer for Saliency Detection in 360{\deg} Videos