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L-TReiD: Logic Tensor Transformer for Re-identification

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Advances in Visual Computing (ISVC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14362))

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Abstract

This article proposes a Neuro-Symbolic (NeSy) machine learning approach to Object Re-identification. NeSy is an emerging branch of artificial intelligence which combines symbolic reasoning and logic-based knowledge representation with the learning capabilities of neural networks. Since object re-identification involves assigning the identity of the same object across different images and different conditions, such a task could benefit greatly from leveraging the logic capabilities of a NeSy framework to inject prior knowledge about invariant properties of the objects. To test this assertion, we combined the Logic Tensor Networks (LTNs) NeSy framework with a state-of-the-art Transformer-based Re-Identification and Damage Detection Network (TransRe3ID). The LTN incorporates prior knowledge about the properties that two instances of the same object have in common. Experimental results on the Bent&Broken Bicycle re-identification dataset demonstrate the potential of LTNs to improve re-identification systems and provide novel opportunities to identify pitfalls during training.

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Acknowledgements

This study was carried out within the FAIR - Future Artificial Intelligence Research and received funding from the European Union Next-GenerationEU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR) - MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.3 - D.D. 1555 11/10/2022, PE00000013). This manuscript reflects only the authors’ views and opinions, neither the European Union nor the European Commission can be considered responsible for them.

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Authors and Affiliations

  1. Politecnico di Torino, 10129, Torino, TO, Italy

    Russo Alessandro, Manigrasso Francesco, Lamberti Fabrizio & Morra Lia

Authors
  1. Russo Alessandro
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  2. Manigrasso Francesco
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  3. Lamberti Fabrizio
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  4. Morra Lia
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Corresponding author

Correspondence to Russo Alessandro .

Editor information

Editors and Affiliations

  1. University of Nevada Reno, Reno, NV, USA

    George Bebis

  2. Google Research, Mountain View, CA, USA

    Golnaz Ghiasi

  3. New York University, New York, USA

    Yi Fang

  4. Ben-Gurion University, Be'er Sheva, Israel

    Andrei Sharf

  5. Microsoft Research, Beijing, China

    Yue Dong

  6. The University of Oklahoma, Norman, OK, USA

    Chris Weaver

  7. University of Maryland, Collage Park, MD, USA

    Zhicheng Leo

  8. University of Central Florida, Orlando, FL, USA

    Joseph J. LaViola Jr.

  9. InnerOptic Technology, Hillsborough, NC, USA

    Luv Kohli

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Alessandro, R., Francesco, M., Fabrizio, L., Lia, M. (2023). L-TReiD: Logic Tensor Transformer for Re-identification. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2023. Lecture Notes in Computer Science, vol 14362. Springer, Cham. https://doi.org/10.1007/978-3-031-47966-3_27

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  • DOI: https://doi.org/10.1007/978-3-031-47966-3_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-47965-6

  • Online ISBN: 978-3-031-47966-3

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