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Motion aware self-supervision for generic event boundary detection

Rai, Ayush K., Krishna, Tarun, Dietlmeier, Julia ORCID: 0000-0001-9980-0910, McGuinness, Kevin ORCID: 0000-0003-1336-6477, Smeaton, Alan F. ORCID: 0000-0003-1028-8389 and O’Connor, Noel E. ORCID: 0000-0002-4033-9135 (2023) Motion aware self-supervision for generic event boundary detection. In: IEEE/CVF Winter Conference on Applications of Computer Vision 2023, 3-7 Jan 2023, Waikoloa, Hawaii. (In Press)

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Abstract

The task of Generic Event Boundary Detection (GEBD) aims to detect moments in videos that are naturally perceived by humans as generic and taxonomy-free event boundaries. Modeling the dynamically evolving temporal and spatial changes in a video makes GEBD a difficult problem to solve. Existing approaches involve very complex and sophisticated pipelines in terms of architectural design choices, hence creating a need for more straightforward and simplified approaches. In this work, we address this issue by revisiting a simple and effective self-supervised method and augment it with a differentiable motion feature learning module to tackle the spatial and temporal diversities in the GEBD task. We perform extensive experiments on the challenging Kinetics-GEBD and TAPOS datasets to demonstrate the efficacy of the proposed approach compared to the other self-supervised state-of-the-art methods. We also show that this simple self-supervised approach learns motion features without any explicit motion-specific pretext task.

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Computer Vision
Subjects:Computer Science > Machine learning
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Published in: 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). . IEEE.
Publisher:IEEE
Official URL:https://doi.org/10.1109/WACV56688.2023.00275
Copyright Information:© 2023 IEEE
Funders:Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289 P2, European Regional Development Fund, Xperi FotoNation
ID Code:28055
Deposited On:26 Jan 2023 16:45 by Ayush Kumar Rai . Last Modified 15 Mar 2023 11:43

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