As detailed in “ WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning”, presented at SIGIR ‘21, this resulted in a curated set of 37.5 million entity-rich image-text examples with 11.5 million unique images across 108 languages. This was accompanied by rigorous filtering to only retain high quality image-text sets.
Today we introduce the Wikipedia-Based Image Text (WIT) Dataset, a large multimodal dataset, created by extracting multiple different text selections associated with an image from Wikipedia articles and Wikimedia image links.
This naturally led us to ask: Can one overcome these limitations and create a high-quality, large-sized, multilingual dataset with a variety of content? An additional shortcoming of existing datasets is the dearth of coverage in non-English languages. On the other hand, the automated extraction approach can lead to bigger datasets, but these require either heuristics and careful filtering to ensure data quality or scaling-up models to achieve strong performance.
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While the former approach tends to result in higher quality data, the intensive manual annotation process limits the amount of data that can be created. Traditionally, these datasets have been created by either manually captioning images, or crawling the web and extracting the alt-text as the caption. Multimodal visio-linguistic models rely on rich datasets in order to model the relationship between images and text. Posted by Krishna Srinivasan, Software Engineer and Karthik Raman, Research Scientist, Google Research
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