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Learning by Aligning 2D Skeleton Sequences and Multi-modality Fusion

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Computer Vision – ECCV 2024 (ECCV 2024)

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Abstract

This paper presents a self-supervised temporal video alignment framework which is useful for several fine-grained human activity understanding applications. In contrast with the state-of-the-art method of CASA, where sequences of 3D skeleton coordinates are taken directly as input, our key idea is to use sequences of 2D skeleton heatmaps as input. Unlike CASA which performs self-attention in the temporal domain only, we feed 2D skeleton heatmaps to a video transformer which performs self-attention both in the spatial and temporal domains for extracting effective spatiotemporal and contextual features. In addition, we introduce simple heatmap augmentation techniques based on 2D skeletons for self-supervised learning. Despite the lack of 3D information, our approach achieves not only higher accuracy but also better robustness against missing and noisy keypoints than CASA. Furthermore, extensive evaluations on three public datasets, i.e., Penn Action, IKEA ASM, and H2O, demonstrate that our approach outperforms previous methods in different fine-grained human activity understanding tasks. Finally, fusing 2D skeleton heatmaps with RGB videos yields the state-of-the-art on all metrics and datasets. To our best knowledge, our work is the first to utilize 2D skeleton heatmap inputs and the first to explore multi-modality fusion for temporal video alignment.

Q.-H. Tran and M. Ahmed—Indicates joint first author.

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Authors and Affiliations

  1. Retrocausal, Inc., Redmond, WA, USA

    Quoc-Huy Tran, Muhammad Ahmed, Murad Popattia, M. Hassan Ahmed, Andrey Konin & M. Zeeshan Zia

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  1. Quoc-Huy Tran
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  2. Muhammad Ahmed
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  3. Murad Popattia
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  4. M. Hassan Ahmed
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  5. Andrey Konin
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  6. M. Zeeshan Zia
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Correspondence to Quoc-Huy Tran .

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Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Germany

    Stefan Roth

  4. Princeton University, Princeton, NJ, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

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Tran, QH., Ahmed, M., Popattia, M., Ahmed, M.H., Konin, A., Zia, M.Z. (2025). Learning by Aligning 2D Skeleton Sequences and Multi-modality Fusion. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15108. Springer, Cham. https://doi.org/10.1007/978-3-031-72973-7_9

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