Abstract
Group Activity Recognition detects the activity collectively performed by a group of actors, which requires compositional reasoning of actors and objects. We approach the task by modeling the video as tokens that represent the multi-scale semantic concepts in the video. We propose COMPOSER, a Multiscale Transformer based architecture that performs attention-based reasoning over tokens at each scale and learns group activity compositionally. In addition, prior works suffer from scene biases with privacy and ethical concerns. We only use the keypoint modality which reduces scene biases and prevents acquiring detailed visual data that may contain private or biased information of users. We improve the multiscale representations in COMPOSER by clustering the intermediate scale representations, while maintaining consistent cluster assignments between scales. Finally, we use techniques such as auxiliary prediction and data augmentations tailored to the keypoint signals to aid model training. We demonstrate the model’s strength and interpretability on two widely-used datasets (Volleyball and Collective Activity). COMPOSER achieves up to + 5.4 % improvement with just the keypoint modality (Code is available at https://github.com/hongluzhou/composer.).
Original language | English |
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Title of host publication | Computer Vision – ECCV 2022 - 17th European Conference, Proceedings |
Editors | Shai Avidan, Gabriel Brostow, Moustapha Cissé, Giovanni Maria Farinella, Tal Hassner |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 249-266 |
Number of pages | 18 |
ISBN (Print) | 9783031198328 |
DOIs | |
State | Published - 2022 |
Event | 17th European Conference on Computer Vision, ECCV 2022 - Tel Aviv, Israel Duration: 23 Oct 2022 → 27 Oct 2022 |
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 | 13695 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 17th European Conference on Computer Vision, ECCV 2022 |
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Country/Territory | Israel |
City | Tel Aviv |
Period | 23/10/22 → 27/10/22 |
Bibliographical note
Publisher Copyright:© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Funding
Acknowledgments. The research was supported in part by NSF awards: IIS-1703883, IIS-1955404, IIS-1955365, RETTL-2119265, and EAGER-2122119. This material is based upon work supported by the U.S. Department of Homeland Security under Grant Award Number 22STESE00001 01 01. Disclaimer: The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Department of Homeland Security.
Funders | Funder number |
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National Science Foundation | IIS-1955404, RETTL-2119265, IIS-1703883, IIS-1955365, EAGER-2122119 |
U.S. Department of Homeland Security | 22STESE00001 01 01 |
Keywords
- Compositionality
- Keypoint-only group activity recognition
- Multiscale representations
- Transformer
- Video understanding