Abstract
Early action prediction aims to successfully predict the class label of an action before it is completely performed. This is a challenging task because the beginning stages of different actions can be very similar, with only minor subtle differences for discrimination. In this paper, we propose a novel Expert Retrieval and Assembly (ERA) module that retrieves and assembles a set of experts most specialized at using discriminative subtle differences, to distinguish an input sample from other highly similar samples. To encourage our model to effectively use subtle differences for early action prediction, we push experts to discriminate exclusively between samples that are highly similar, forcing these experts to learn to use subtle differences that exist between those samples. Additionally, we design an effective Expert Learning Rate Optimization method that balances the experts’ optimization and leads to better performance. We evaluate our ERA module on four public action datasets and achieve state-of-the-art performance.
Original language | English |
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Title of host publication | 17th European Conference Tel Aviv, Israel, October 23–27, 2022 Proceedings, Part XXXIV |
Editors | Shai Avidan, Gabriel Brostow, Moustapha Cisse, Giovanni Maria Farinella, Tal Hassner |
Place of Publication | Cham Switzerland |
Publisher | Springer |
Pages | 670–688 |
Number of pages | 19 |
ISBN (Electronic) | 9783031198304 |
ISBN (Print) | 9783031198298 |
DOIs | |
Publication status | Published - 2022 |
Event | European Conference on Computer Vision 2022 - Tel Aviv, Israel Duration: 23 Oct 2022 → 27 Oct 2022 Conference number: 17th https://link.springer.com/book/10.1007/978-3-031-19830-4 (Proceedings) https://eccv2022.ecva.net (Website) |
Conference
Conference | European Conference on Computer Vision 2022 |
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Abbreviated title | ECCV 2022 |
Country/Territory | Israel |
City | Tel Aviv |
Period | 23/10/22 → 27/10/22 |
Internet address |
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Keywords
- Early action prediction
- Dynamic networks
- Expert retrieval