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In 2016, Reed, Akata, Yan et al. became the first to use generative adversarial networks for the text-to-image task. [5] [7] With models trained on narrow, domain-specific datasets, they were able to generate "visually plausible" images of birds and flowers from text captions like "an all black bird with a distinct thick, rounded bill".
CLIP is a separate model based on zero-shot learning that was trained on 400 million pairs of images with text captions scraped from the Internet. [ 5 ] [ 23 ] [ 24 ] Its role is to "understand and rank" DALL·E's output by predicting which caption from a list of 32,768 captions randomly selected from the dataset (of which one was the correct ...
The Tesla Experimental Station[ 1] was a laboratory in Colorado Springs, Colorado, USA built in 1899 by inventor Nikola Tesla and for his study of the use of high-voltage, high-frequency electricity in wireless power transmission. Tesla used it for only one year, until 1900, and it was torn down in 1904 to pay his outstanding debts.
A caption is text that appears below an image. a Most captions draw attention to something in the image that is not obvious, such as its relevance to the text. A caption may be a few words or several sentences. Writing good captions takes effort; along with the lead and section headings, captions are the most commonly read words in an article ...
Where open captions are used (generally in instances where the speaker is not easily understood) a blank is used where the word is bleeped. Occasionally, bleeping is not reflected in the captions, allowing the unedited dialogue to be seen. [citation needed] Sometimes, a "black bar" can be seen for a closed caption bleep. [5] [better source needed]
An infobox is a fixed-format table usually added to the top right-hand corner of articles to consistently present a summary of some unifying aspect that the articles share and sometimes to improve navigation to other interrelated articles. Many infoboxes also emit structured metadata which is sourced by DBpedia and other third party re-users.
The symbol generator can go by other names, such as display processing computer, display electronics unit, etc. The symbol generator does more than generate symbols. It has (at the least) monitoring facilities, a graphics generator and a display driver. [2] Inputs from sensors and controls arrive via data buses, and are checked for validity.
The success in image classification was then extended to the more challenging task of generating descriptions (captions) for images, often as a combination of CNNs and LSTMs. [108] [109] [110] In 2014, the state of the art was training “very deep neural network” with 20 to 30 layers. [111]
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