Putting Humans in the Image Captioning Loop

• URL: http://arxiv.org/abs/2306.03476v1
• Date: Tue, 6 Jun 2023 07:50:46 GMT
• Title: Putting Humans in the Image Captioning Loop
• Authors: Aliki Anagnostopoulou and Mareike Hartmann and Daniel Sonntag
• Abstract summary: We present work-in-progress on adapting an IC system to integrate human feedback. Our approach builds on a base IC model pre-trained on the MS COCO dataset, which generates captions for unseen images. We hope that this approach, while leading to improved results, will also result in customizable IC models.
• Score: 8.584932159968002
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Image Captioning (IC) models can highly benefit from human feedback in the training process, especially in cases where data is limited. We present work-in-progress on adapting an IC system to integrate human feedback, with the goal to make it easily adaptable to user-specific data. Our approach builds on a base IC model pre-trained on the MS COCO dataset, which generates captions for unseen images. The user will then be able to offer feedback on the image and the generated/predicted caption, which will be augmented to create additional training instances for the adaptation of the model. The additional instances are integrated into the model using step-wise updates, and a sparse memory replay component is used to avoid catastrophic forgetting. We hope that this approach, while leading to improved results, will also result in customizable IC models.

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