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 study Multiscale Vision Transformers (MViTv2) as a unifiedarchitecture for image and video classification, as well as object detection.We present an improved version of MViT that incorporates decomposed relativepositional embeddings and residual pooling connections. We instantiate thisarchitecture in five sizes and evaluate it for ImageNet classification, COCOdetection and Kinetics video recognition where it outperforms prior work. Wefurther compare MViTv2s' pooling attention to window attention mechanisms whereit outperforms the latter in accuracy/compute. Without bells-and-whistles,MViTv2 has state-of-the-art performance in 3 domains: 88.8% accuracy onImageNet classification, 58.7 boxAP on COCO object detection as well as 86.1%on Kinetics-400 video classification. Code and models are available athttps://github.com/facebookresearch/mvit.

MViTv2: Improved Multiscale Vision Transformers for Classification and Detection