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

Multi-person pose estimation from a 2D image is challenging because itrequires not only keypoint localization but also human detection. Instate-of-the-art top-down methods, multi-scale information is a crucial factorfor the accurate pose estimation because it contains both of local informationaround the keypoints and global information of the entire person. Althoughmulti-scale information allows these methods to achieve the state-of-the-artperformance, the top-down methods still require a huge amount of computationbecause they need to use an additional human detector to feed the cropped humanimage to their pose estimation model. To effectively utilize multi-scaleinformation with the smaller computation, we propose a multi-scale aggregationR-CNN (MSA R-CNN). It consists of multi-scale RoIAlign block (MS-RoIAlign) andmulti-scale keypoint head network (MS-KpsNet) which are designed to effectivelyutilize multi-scale information. Also, in contrast to previous top-downmethods, the MSA R-CNN performs human detection and keypoint localization in asingle model, which results in reduced computation. The proposed model achievedthe best performance among single model-based methods and its results arecomparable to those of separated model-based methods with a smaller amount ofcomputation on the publicly available 2D multi-person keypoint localizationdataset.

Multi-scale Aggregation R-CNN for 2D Multi-person Pose Estimation