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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

End-to-end paradigms significantly improve the accuracy of variousdeep-learning-based computer vision models. To this end, tasks like objectdetection have been upgraded by replacing non-end-to-end components, such asremoving non-maximum suppression by training with a set loss based on bipartitematching. However, such an upgrade is not applicable to instance segmentation,due to its significantly higher output dimensions compared to object detection.In this paper, we propose an instance segmentation Transformer, termed ISTR,which is the first end-to-end framework of its kind. ISTR predictslow-dimensional mask embeddings, and matches them with ground truth maskembeddings for the set loss. Besides, ISTR concurrently conducts detection andsegmentation with a recurrent refinement strategy, which provides a new way toachieve instance segmentation compared to the existing top-down and bottom-upframeworks. Benefiting from the proposed end-to-end mechanism, ISTRdemonstrates state-of-the-art performance even with approximation-basedsuboptimal embeddings. Specifically, ISTR obtains a 46.8/38.6 box/mask AP usingResNet50-FPN, and a 48.1/39.9 box/mask AP using ResNet101-FPN, on the MS COCOdataset. Quantitative and qualitative results reveal the promising potential ofISTR as a solid baseline for instance-level recognition. Code has been madeavailable at: https://github.com/hujiecpp/ISTR.

ISTR: End-to-End Instance Segmentation with Transformers