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Towards Adaptable and Interactive Image Captioning with Data Augmentation and Episodic Memory
Interactive machine learning (IML) is a beneficial learning paradigm in cases of limited data availability, as human feedback is incrementally integrated into the training process.
Putting Humans in the Image Captioning Loop
Image Captioning (IC) models can highly benefit from human feedback in the training process, especially in cases where data is limited.
Interactive Machine Learning for Image Captioning
We propose an approach for interactive learning for an image captioning model.
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