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 develop an efficient multi-scale network to predict actionclasses in partial videos in an end-to-end manner. Unlike most existing methodswith offline feature generation, our method directly takes frames as input andfurther models motion evolution on two different temporal scales.Therefore, wesolve the complexity problems of the two stages of modeling and the problem ofinsufficient temporal and spatial information of a single scale. Our proposedEnd-to-End MultiScale Network (E2EMSNet) is composed of two scales which arenamed segment scale and observed global scale. The segment scale leveragestemporal difference over consecutive frames for finer motion patterns bysupplying 2D convolutions. For observed global scale, a Long Short-Term Memory(LSTM) is incorporated to capture motion features of observed frames. Our modelprovides a simple and efficient modeling framework with a small computationalcost. Our E2EMSNet is evaluated on three challenging datasets: BIT, HMDB51, andUCF101. The extensive experiments demonstrate the effectiveness of our methodfor action prediction in videos.

An end-to-end multi-scale network for action prediction in videos