(5 September 2019) The web is filled with billions of images, helping to entertain and inform the world on a countless variety of subjects. However, much of that visual information is not accessible to those with visual impairments, or with slow internet speeds that prohibit the loading of images.
Image captions, manually added by website authors using Alt-text HTML, is one way to make this content more accessible, so that a natural-language description for images that can be presented using text-to-speech systems. However, existing human-curated Alt-text HTML fields are added for only a very small fraction of web images. And while automatic image captioning can help solve this problem, accurate image captioning is a challenging task that requires advancing the state of the art of both computer vision and natural language processing.
Today Google introduces Conceptual Captions, a new dataset consisting of ~3.3 million image/caption pairs that are created by automatically extracting and filtering image caption annotations from billions of web pages. Introduced in a paper presented at ACL 2018, Conceptual Captions represents an order of magnitude increase of captioned images over the human-curated MS-COCO dataset. As measured by human raters, the machine-curated Conceptual Captions has an accuracy of ~90%. Furthermore, because images in Conceptual Captions are pulled from across the web, it represents a wider variety of image-caption styles than previous datasets, allowing for better training of image captioning models. To track progress on image captioning, we are also announcing the Conceptual Captions Challenge for the machine learning community to train and evaluate their own image captioning models on the Conceptual Captions test bed.
The Google Blog has all the details.
