Abstract
Online Temporal Action Localization (On-TAL) is a critical task that aims to instantaneously identify action instances in untrimmed streaming videos as soon as an action concludes—a major leap from frame-based Online Action Detection (OAD). Yet, the challenge of detecting overlapping actions is often overlooked even though it is a common scenario in streaming videos. Current methods that can address concurrent actions depend heavily on class information, limiting their flexibility. This paper introduces ActionSwitch, the first class-agnostic On-TAL framework capable of detecting overlapping actions. By obviating the reliance on class information, ActionSwitch provides wider applicability to various situations, including overlapping actions of the same class or scenarios where class information is unavailable. This approach is complemented by the proposed “conservativeness loss”, which directly embeds a conservative decision-making principle into the loss function for On-TAL. Our ActionSwitch achieves state-of-the-art performance in complex datasets, including Epic-Kitchens 100 targeting the challenging egocentric view and FineAction consisting of fine-grained actions.
Original language | English |
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Title of host publication | Computer Vision – ECCV 2024 - 18th European Conference, Proceedings |
Editors | Aleš Leonardis, Elisa Ricci, Stefan Roth, Olga Russakovsky, Torsten Sattler, Gül Varol |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 383-400 |
Number of pages | 18 |
ISBN (Print) | 9783031726835 |
DOIs | |
Publication status | Published - 2025 |
Event | 18th European Conference on Computer Vision, ECCV 2024 - Milan, Italy Duration: 2024 Sept 29 → 2024 Oct 4 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 15068 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 18th European Conference on Computer Vision, ECCV 2024 |
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Country/Territory | Italy |
City | Milan |
Period | 24/9/29 → 24/10/4 |
Bibliographical note
Publisher Copyright:© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- General Computer Science