Denoising Large-Scale Image Captioning from Alt-text Data using Content Selection Models

• URL: http://arxiv.org/abs/2009.05175v2
• Date: Fri, 16 Apr 2021 23:11:48 GMT
• Title: Denoising Large-Scale Image Captioning from Alt-text Data using Content Selection Models
• Authors: Khyathi Raghavi Chandu, Piyush Sharma, Soravit Changpinyo, Ashish Thapliyal, Radu Soricut
• Abstract summary: We show that selecting content words as skeletons helps in generating improved and denoised captions. We also show that the predicted English skeletons can be further cross-lingually leveraged to generate non-English captions. We also show that skeleton-based prediction allows for better control of certain caption properties, such as length, content, and gender expression.
• Score: 25.86785379429413
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Training large-scale image captioning (IC) models demands access to a rich and diverse set of training examples, gathered from the wild, often from noisy alt-text data. However, recent modeling approaches to IC often fall short in terms of performance in this case, because they assume a clean annotated dataset (as opposed to the noisier alt-text--based annotations), and employ an end-to-end generation approach, which often lacks both controllability and interpretability. We address these problems by breaking down the task into two simpler, more controllable tasks -- skeleton prediction and skeleton-based caption generation. Specifically, we show that selecting content words as skeletons} helps in generating improved and denoised captions when leveraging rich yet noisy alt-text--based uncurated datasets. We also show that the predicted English skeletons can be further cross-lingually leveraged to generate non-English captions, and present experimental results covering caption generation in French, Italian, German, Spanish and Hindi. We also show that skeleton-based prediction allows for better control of certain caption properties, such as length, content, and gender expression, providing a handle to perform human-in-the-loop semi-automatic corrections.

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