#DUAL FAMILY PIC MANUAL#

#DUAL FAMILY PIC MANUAL#

To get around this problem, manual collection of imageвЂ“text pair data should be done for under-resourced languages. In fact, the top ten languages with the most data, such as English and Chinese, account for more than 90% of this sort of online data, with substantially less data for under-resourced languages. Unfortunately, for most languages, comparable imageвЂ“text pair data does not exist at the same scale. For example, ALIGN and CLIP have shown that when given enough training data, training a dual-encoder model (i.e., one with two independent encoders) on image-text pairs using a contrastive learning loss works exceptionally well. These representation models aim to store the picture and text as vectors in a shared embedding space where the image and the text describing it are close to each other.

For high-resource languages like English, previous research has made significant progress in learning imageвЂ“text combined representations. It is now feasible to eliminate ambiguity in translation by displaying a text combined with a supporting image, thanks to recent developments in neural machine translation and image recognition. In English, a bride in a white gown and a groom in a tuxedo are frequently associated, however in Hindi (а¤¶а¤ѕа¤¦аҐЂ), a bride in brilliant colors and a guy in a sherwani may be a more fitting association. Although each personвЂ™s associations with the term may differ significantly, the meaning becomes more evident when they are presented with a visual of the intended concept. However, when the idea is anchored in visual examples, the meaning may be more evident. Even when there is, such translations typically contain various connections and meanings that a non-native speaker would easily miss. There is no straight one-to-one translation from one language to another for many concepts.

Dual family pic