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Discerning Generic Event Boundaries in Long-Form Wild Videos

Publication Type: 
Edited Conference Meeting Proceeding
Detecting generic, taxonomy-free event boundaries invideos represents a major stride forward towards holisticvideo understanding. In this paper we present a technique forgeneric event boundary detection based on a two stream in-flated 3D convolutions architecture, which can learn spatio-temporal features from videos. Our work is inspired from theGeneric Event Boundary Detection Challenge (part of CVPR2021 Long Form Video Understanding- LOVEU Workshop).Throughout the paper we provide an in-depth analysis ofthe experiments performed along with an interpretation ofthe results obtained.
Conference Name: 
LOVEU Challenge (CVPR 2021)
Digital Object Identifer (DOI):
Publication Date: 
Conference Location: 
United Kingdom (excluding Northern Ireland)
Research Group: 
Dublin City University (DCU)
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