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Demystifying Unsupervised Semantic Correspondence Estimation

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13690))

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Abstract

We explore semantic correspondence estimation through the lens of unsupervised learning. We thoroughly evaluate several recently proposed unsupervised methods across multiple challenging datasets using a standardized evaluation protocol where we vary factors such as the backbone architecture, the pre-training strategy, and the pre-training and finetuning datasets. To better understand the failure modes of these methods, and in order to provide a clearer path for improvement, we provide a new diagnostic framework along with a new performance metric that is better suited to the semantic matching task. Finally, we introduce a new unsupervised correspondence approach which utilizes the strength of pre-trained features while encouraging better matches during training. This results in significantly better matching performance compared to current state-of-the-art methods.

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Acknowledgements

Thanks to Hakan Bilen and Omiros Pantazis for their valuable feedback. This work was in part supported by the Turing 2.0 ‘Enabling Advanced Autonomy’ project funded by the EPSRC and the Alan Turing Institute.

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Authors and Affiliations

  1. University of Edinburgh, Edinburgh, UK

    Mehmet Aygün & Oisin Mac Aodha

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  1. Mehmet Aygün
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  2. Oisin Mac Aodha
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Correspondence to Mehmet Aygün .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Aygün, M., Mac Aodha, O. (2022). Demystifying Unsupervised Semantic Correspondence Estimation. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13690. Springer, Cham. https://doi.org/10.1007/978-3-031-20056-4_8

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  • DOI: https://doi.org/10.1007/978-3-031-20056-4_8

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