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

Most existing transformer based video instance segmentation methods extractper frame features independently, hence it is challenging to solve theappearance deformation problem. In this paper, we observe the temporalinformation is important as well and we propose TAFormer to aggregatespatio-temporal features both in transformer encoder and decoder. Specifically,in transformer encoder, we propose a novel spatio-temporal joint multi-scaledeformable attention module which dynamically integrates the spatial andtemporal information to obtain enriched spatio-temporal features. Intransformer decoder, we introduce a temporal self-attention module to enhancethe frame level box queries with the temporal relation. Moreover, TAFormeradopts an instance level contrastive loss to increase the discriminability ofinstance query embeddings. Therefore the tracking error caused by visuallysimilar instances can be decreased. Experimental results show that TAFormereffectively leverages the spatial and temporal information to obtaincontext-aware feature representation and outperforms state-of-the-art methods.

Towards Robust Video Instance Segmentation with Temporal-Aware Transformer