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

Identifying relations between objects is central to understanding the scene.While several works have been proposed for relation modeling in the imagedomain, there have been many constraints in the video domain due to challengingdynamics of spatio-temporal interactions (e.g., between which objects are therean interaction? when do relations start and end?). To date, two representativemethods have been proposed to tackle Video Visual Relation Detection (VidVRD):segment-based and window-based. We first point out limitations of these methodsand propose a novel approach named Temporal Span Proposal Network (TSPN). TSPNtells what to look: it sparsifies relation search space by scoring relationnessof object pair, i.e., measuring how probable a relation exist. TSPN tells whento look: it simultaneously predicts start-end timestamps (i.e., temporal spans)and categories of the all possible relations by utilizing full video context.These two designs enable a win-win scenario: it accelerates training by 2X ormore than existing methods and achieves competitive performance on two VidVRDbenchmarks (ImageNet-VidVDR and VidOR). Moreover, comprehensive ablativeexperiments demonstrate the effectiveness of our approach. Codes are availableat https://github.com/sangminwoo/Temporal-Span-Proposal-Network-VidVRD.

What and When to Look?: Temporal Span Proposal Network for Video Relation Detection