An illustration of how the image text contrastive and translation text contrastive tasks work together to help align the space of images, English text and non-English text. On the left side of the illustration, the three domains—Image Domain, English Domain, and Non-English Domain--are segregated. An arrow labeled “Image-Captions training data” points to another depiction of the three domains where the image domain and the English domain intersect but the non-English domain is still separate and shown in gray to show that it’s not significantly affected. A two headed arrow with the label “Image-Text contrastive loss” is drawn between the image and English domains. Towards the bottom of the image, an arrow labeled “Parallel corpus training data” points to another depiction of the three domains where the English domain and the non-English domain intersect but the image domain is separate and shown in gray to indicate that it is not significantly affected. A two-headed arrow with the label “Translated Text Contrastive loss” is drawn between the English and non-English domains. Finally, a third arrow with the label “Resulting Effect” is drawn to the right of the image which points to a depiction of all three domains intersecting.

Turing Bletchley: A Universal Image Language Representation model by Microsoft

Today, the Microsoft Turing team (opens in new tab) is thrilled to introduce Turing Bletchley, a 2.5-billion parameter Universal Image Language Representation model (T-UILR) that can perform image-language tasks in 94 languages. T-Bletchley has an image encoder and a universal language encoder that vectorize input image and text respectively so that semantically similar images and texts align with each … Continue reading Turing Bletchley: A Universal Image Language Representation model by Microsoft

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