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A Framework for Explanation-Aware Visualization and Adjudication in Object Detection: First Results and Perspectives

  • Conference paper
  • First Online:
Artificial Intelligence for Neuroscience and Emotional Systems (IWINAC 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14674))

  • 496 Accesses

Abstract

Context-aware systems require context information, which essentially should rely on high-quality data. Object detection is one particular area enabling context information from the environment to be processed. Ensuring the presence of high-quality data is crucial for machine learning methods to detect objects with high precision. This paper presents a framework for explanation-aware visualization and adjudication in object detection, integrating the user into a semi-automatic verification and adjudication process, where targeted information can be transported by visualization and explanation methods. We discuss a tool for supporting such approaches and present first results and perspectives.

A. G. Chowdhury and D. Massanés—Both authors contributed equally to this work.

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Notes

  1. 1.

    VizAOD source code: https://github.com/cslab-hub/vizaod.

  2. 2.

    https://github.com/facebookresearch/detectron2.

  3. 3.

    https://github.com/cslab-hub/MatrixDataExtractor/tree/main/tabledetection.

  4. 4.

    https://github.com/ultralytics/ultralytics.

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Acknowledgements

This work has been supported by the funded project FRED, German Federal Ministry for Economic Affairs and Climate Action (BMWK), FKZ: 01MD22003E.

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Correspondence to Martin Atzmueller .

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Chowdhury, A.G., Massanés, D., Meinert, S., Atzmueller, M. (2024). A Framework for Explanation-Aware Visualization and Adjudication in Object Detection: First Results and Perspectives. In: Ferrández Vicente, J.M., Val Calvo, M., Adeli, H. (eds) Artificial Intelligence for Neuroscience and Emotional Systems. IWINAC 2024. Lecture Notes in Computer Science, vol 14674. Springer, Cham. https://doi.org/10.1007/978-3-031-61140-7_47

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  • DOI: https://doi.org/10.1007/978-3-031-61140-7_47

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