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01_caption_preprocessing

Image processing Image analysis Image captioning Deep learning Neural networks
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Benjamin Wilhelm profile image

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In the last workflow of this series, we predict the captions of the images in our test sample. This can be done using a similar iterative approach, meaning predicting the captions word by word. First, we create a seed by creating a partial caption only containing the special start token. This can then be input into the network to predict the first word of the caption. Using the predicted word, a new partial caption is created, and the process is repeated until the special end toke is predicted. In KNIME, this is implemented in a Recursive Loop. In each loop iteration we check if the end token has been predicted. If this is the case, we exclude the corresponding row from further iteration as the full caption has already been predicted. All other rows are used for the next loop iteration. This is done until the end token has been predicted for all examples.

External resources

  • Image Captioning
  • COCO 2014 Data Download
  • COCO Dataset Homepage

Used extensions & nodes

Created with KNIME Analytics Platform version 4.5.2
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    KNIME Base nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Versions 4.1.2, 4.5.2

    knime
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    KNIME Deep Learning - Keras Integration Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.1.0

    knime
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    KNIME Expressions Trusted extension

    KNIME AG, Zurich, Switzerland

    Versions 4.1.0, 4.5.1

    knime
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    KNIME Image Processing Trusted extension

    University of Konstanz / KNIME

    Version 1.8.1

    bioml-konstanz
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    KNIME Python Integration Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.1.1

    knime
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    KNIME Textprocessing Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.1.0

    knime
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