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Adaptive High-Frequency Transformer for Diverse Wildlife Re-identification

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Computer Vision – ECCV 2024 (ECCV 2024)

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Abstract

Wildlife ReID involves utilizing visual technology to identify specific individuals of wild animals in different scenarios, holding significant importance for wildlife conservation, ecological research, and environmental monitoring. Existing wildlife ReID methods are predominantly tailored to specific species, exhibiting limited applicability. Although some approaches leverage extensively studied person ReID techniques, they struggle to address the unique challenges posed by wildlife. Therefore, in this paper, we present a unified, multi-species general framework for wildlife ReID. Given that high-frequency information is a consistent representation of unique features in various species, significantly aiding in identifying contours and details such as fur textures, we propose the Adaptive High-Frequency Transformer model with the goal of enhancing high-frequency information learning. To mitigate the inevitable high-frequency interference in the wilderness environment, we introduce an object-aware high-frequency selection strategy to adaptively capture more valuable high-frequency components. Notably, we unify the experimental settings of multiple wildlife datasets for ReID, achieving superior performance over state-of-the-art ReID methods. In domain generalization scenarios, our approach demonstrates robust generalization to unknown species. Code is available at https://github.com/JigglypuffStitch/AdaFreq.git.

C. Li and S. Chen—Equal contributions.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China under Grant (62176188, 62361166629) and the Special Fund of Hubei Luojia Laboratory (220100015). The numerical calculations in this paper have been done on the supercomputing system in the Supercomputing Center of Wuhan University.

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Authors and Affiliations

  1. National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence, School of Computer Science, Hubei Luojia Laboratory, Wuhan University, Wuhan, China

    Chenyue Li, Shuoyi Chen & Mang Ye

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  1. Chenyue Li
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  2. Shuoyi Chen
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Correspondence to Mang Ye .

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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, Hessen, Germany

    Stefan Roth

  4. Princeton University, Palo Alto, CA, 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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Li, C., Chen, S., Ye, M. (2025). Adaptive High-Frequency Transformer for Diverse Wildlife Re-identification. 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 15102. Springer, Cham. https://doi.org/10.1007/978-3-031-72784-9_17

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