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

This work addresses the issue of image classification and localization of human actions based on visual data acquired from RGB sensors. Our approach is inspired by the success of deep learning in image classification. In this paper, we describe our method and how the concept of Global Average Pooling (GAP) applies in the context of semi-supervised class localization. We benchmark it with respect to Class Activation Mapping initiated in (Zhou et al., 2016), propose a regularization over the GAP maps to enhance the results, and study whether a combination of these two ideas can result in a better classification accuracy. The models are trained and tested on the Stanford 40 Action dataset (Yao et al., 2011) describing people performing 40 different actions such as drinking, cooking or watching TV. Compared to the aforementioned baseline, our model improves the classification accuracy by 5.3 percent points, achieves a localization accuracy of 50.3%, and drastically diminishes the computation needed to retrieve the class saliency from the base convolutional model.

Mind the Regularized GAP, for Human Action Classification and Semi-supervised Localization based on Visual Saliency