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

Video captioning, i.e. the task of generating captions from video sequencescreates a bridge between the Natural Language Processing and Computer Visiondomains of computer science. The task of generating a semantically accuratedescription of a video is quite complex. Considering the complexity, of theproblem, the results obtained in recent research works are praiseworthy.However, there is plenty of scope for further investigation. This paperaddresses this scope and proposes a novel solution. Most video captioningmodels comprise two sequential/recurrent layers - one as a video-to-contextencoder and the other as a context-to-caption decoder. This paper proposes anovel architecture, namely Semantically Sensible Video Captioning (SSVC) whichmodifies the context generation mechanism by using two novel approaches -"stacked attention" and "spatial hard pull". As there are no exclusive metricsfor evaluating video captioning models, we emphasize both quantitative andqualitative analysis of our model. Hence, we have used the BLEU scoring metricfor quantitative analysis and have proposed a human evaluation metric forqualitative analysis, namely the Semantic Sensibility (SS) scoring metric. SSScore overcomes the shortcomings of common automated scoring metrics. Thispaper reports that the use of the aforementioned novelties improves theperformance of state-of-the-art architectures.

Video captioning with stacked attention and semantic hard pull