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 propose fill-in-the-blanks as a video understanding evaluation frameworkand introduce FIBER -- a novel dataset consisting of 28,000 videos anddescriptions in support of this evaluation framework. The fill-in-the-blankssetting tests a model's understanding of a video by requiring it to predict amasked noun phrase in the caption of the video, given the video and thesurrounding text. The FIBER benchmark does not share the weaknesses of thecurrent state-of-the-art language-informed video understanding tasks, namely:(1) video question answering using multiple-choice questions, where modelsperform relatively well because they exploit linguistic biases in the taskformulation, thus making our framework challenging for the currentstate-of-the-art systems to solve; and (2) video captioning, which relies on anopen-ended evaluation framework that is often inaccurate because system answersmay be perceived as incorrect if they differ in form from the ground truth. TheFIBER dataset and our code are available at https://lit.eecs.umich.edu/fiber/.

FIBER: Fill-in-the-Blanks as a Challenging Video Understanding Evaluation Framework