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Referring Atomic Video Action Recognition

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Computer Vision – ECCV 2024 (ECCV 2024)

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Abstract

We introduce a new task called Referring Atomic Video Action Recognition (RAVAR), aimed at identifying atomic actions of a particular person based on a textual description and the video data of this person. This task differs from traditional action recognition and localization, where predictions are delivered for all present individuals. In contrast, we focus on recognizing the correct atomic action of a specific individual, guided by text. To explore this task, we present the RefAVA dataset, containing 36, 630 instances with manually annotated textual descriptions of the individuals. To establish a strong initial benchmark, we implement and validate baselines from various domains, e.g., atomic action localization, video question answering, and text-video retrieval. Since these existing methods underperform on RAVAR, we introduce RefAtomNet – a novel cross-stream attention-driven method specialized for the unique challenges of RAVAR: the need to interpret a textual referring expression for the targeted individual, utilize this reference to guide the spatial localization and harvest the prediction of the atomic actions for the referring person. The key ingredients are: (1) a multi-stream architecture that connects video, text, and a new location-semantic stream, and (2) cross-stream agent attention fusion and agent token fusion which amplify the most relevant information across these streams and consistently surpasses standard attention-based fusion on RAVAR. Extensive experiments demonstrate the effectiveness of RefAtomNet and its building blocks for recognizing the action of the described individual. The dataset and code will be made publicly available at RAVAR.

K. Peng and J. Fu—Equal contribution.

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Acknowledgements

The project served to prepare the SFB 1574 Circular Factory for the Perpetual Product (project ID: 471687386), approved by the German Research Foundation (DFG, German Research Foundation) with a start date of April 1, 2024. This work was also partially supported in part by the SmartAge project sponsored by the Carl Zeiss Stiftung (P2019-01-003; 2021–2026). This work was performed on the HoreKa supercomputer funded by the Ministry of Science, Research and the Arts Baden-W"urttemberg and by the Federal Ministry of Education and Research. The authors also acknowledge support by the state of Baden-W"urttemberg through bwHPC and the German Research Foundation (DFG) through grant INST 35/1597-1 FUGG. This project is also supported by the National Key RD Program under Grant 2022YFB4701400. Lastly, the authors thank for the support of Dr. Sepideh Pashami, the Swedish Innovation Agency VINNOVA, the Digital Futures.

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Authors and Affiliations

  1. Karlsruhe Institute of Technology, Karlsruhe, Germany

    Kunyu Peng, Di Wen, Yufan Chen, Ruiping Liu, Junwei Zheng, Jiaming Zhang, M. Saquib Sarfraz & Rainer Stiefelhagen

  2. Hunan University, Changsha, China

    Kailun Yang

  3. RISE Research Institutes of Sweden, Gothenburg, Sweden

    Jia Fu

  4. KTH Royal Institute of Technology, Stockholm, Sweden

    Jia Fu

  5. University of Stuttgart, Stuttgart, Germany

    Alina Roitberg

  6. Mercedes-Benz Tech Innovation, Ulm, Germany

    M. Saquib Sarfraz

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  1. Kunyu Peng
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Correspondence to Kailun Yang .

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  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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Peng, K. et al. (2025). Referring Atomic Video Action Recognition. 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 15077. Springer, Cham. https://doi.org/10.1007/978-3-031-72655-2_10

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