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

We motivate and address a human-in-the-loop variant of the monocularviewpoint estimation task in which the location and class of one semanticobject keypoint is available at test time. In order to leverage the keypointinformation, we devise a Convolutional Neural Network called Click-Here CNN(CH-CNN) that integrates the keypoint information with activations from thelayers that process the image. It transforms the keypoint information into a 2Dmap that can be used to weigh features from certain parts of the image moreheavily. The weighted sum of these spatial features is combined with globalimage features to provide relevant information to the prediction layers. Totrain our network, we collect a novel dataset of 3D keypoint annotations onthousands of CAD models, and synthetically render millions of images with 2Dkeypoint information. On test instances from PASCAL 3D+, our model achieves amean class accuracy of 90.7%, whereas the state-of-the-art baseline onlyobtains 85.7% mean class accuracy, justifying our argument forhuman-in-the-loop inference.

Click Here: Human-Localized Keypoints as Guidance for Viewpoint Estimation