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

Anonymous

Abstract: Training large-scale image captioning (IC) models demands access to a rich and diverse set of training examples that are expensive to curate both in terms of time and man-power. Instead, alt-text based captions gathered from the web is a far cheaper alternative to scale with the downside of being noisy. Recent modeling approaches to IC often fall short in terms of performance in leveraging these noisy datasets in favor of clean annotations. We address this problem by breaking down the task into two simpler, more controllable tasks -- skeleton prediction and skeleton-based caption generation. Specifically, we show that sub-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 further cross-lingually be 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 interpretable semi-automatic corrections.