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

The referring video object segmentation task (RVOS) aims to segment objectinstances in a given video referred by a language expression in all videoframes. Due to the requirement of understanding cross-modal semantics withinindividual instances, this task is more challenging than the traditionalsemi-supervised video object segmentation where the ground truth object masksin the first frame are given. With the great achievement of Transformer inobject detection and object segmentation, RVOS has been made remarkableprogress where ReferFormer achieved the state-of-the-art performance. In thiswork, based on the strong baseline framework--ReferFormer, we propose severaltricks to boost further, including cyclical learning rates, semi-supervisedapproach, and test-time augmentation inference. The improved ReferFormer ranks2nd place on CVPR2022 Referring Youtube-VOS Challenge.

The Second Place Solution for The 4th Large-scale Video Object Segmentation Challenge--Track 3: Referring Video Object Segmentation