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

Natural language video localization (NLVL) is an important task in thevision-language understanding area, which calls for an in-depth understandingof not only computer vision and natural language side alone, but moreimportantly the interplay between both sides. Adversarial vulnerability hasbeen well-recognized as a critical security issue of deep neural networkmodels, which requires prudent investigation. Despite its extensive yetseparated studies in video and language tasks, current understanding of theadversarial robustness in vision-language joint tasks like NLVL is lessdeveloped. This paper therefore aims to comprehensively investigate theadversarial robustness of NLVL models by examining three facets ofvulnerabilities from both attack and defense aspects. To achieve the attackgoal, we propose a new adversarial attack paradigm called synonymoussentences-aware adversarial attack on NLVL (SNEAK), which captures thecross-modality interplay between the vision and language sides.

SNEAK: Synonymous Sentences-Aware Adversarial Attack on Natural Language Video Localization