Interactive Machine Learning for Image Captioning

• URL: http://arxiv.org/abs/2202.13623v1
• Date: Mon, 28 Feb 2022 09:02:32 GMT
• Title: Interactive Machine Learning for Image Captioning
• Authors: Mareike Hartmann, Aliki Anagnostopoulou, Daniel Sonntag
• Abstract summary: We propose an approach for interactive learning for an image captioning model. We envision a system that exploits human feedback as good as possible by multiplying the feedback using data augmentation methods.
• Score: 8.584932159968002
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We propose an approach for interactive learning for an image captioning model. As human feedback is expensive and modern neural network based approaches often require large amounts of supervised data to be trained, we envision a system that exploits human feedback as good as possible by multiplying the feedback using data augmentation methods, and integrating the resulting training examples into the model in a smart way. This approach has three key components, for which we need to find suitable practical implementations: feedback collection, data augmentation, and model update. We outline our idea and review different possibilities to address these tasks.

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