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Output of DenseCap "dense captioning" software, analysing a photograph of a man riding an elephant. Automatic image annotation (also known as automatic image tagging or linguistic indexing) is the process by which a computer system automatically assigns metadata in the form of captioning or keywords to a digital image.
Computer Vision Annotation Tool (CVAT) is a free, open source, web-based image and video annotation tool used for labeling data for computer vision algorithms. Originally developed by Intel , CVAT is designed for use by a professional data annotation team, with a user interface optimized for computer vision annotation tasks.
Manual image annotation is the process of manually defining regions in an image and creating a textual description of those regions. Such annotations can for instance be used to train machine learning algorithms for computer vision applications. This is a list of computer software which can be used for manual annotation of images.
Captions is a video-editing and AI research company headquartered in New York City. Their flagship app, Captions , is available on iOS , Android , and Web and offers a suite of tools aimed at streamlining the creation and editing of videos.
An image conditioned on the prompt "an astronaut riding a horse, by Hiroshige", generated by Stable Diffusion 3.5, a large-scale text-to-image model first released in 2022. A text-to-image model is a machine learning model which takes an input natural language description and produces an image matching that description.
Further, one can take a list of caption-image pairs, convert the images into strings of symbols, and train a standard GPT-style transformer. Then at test time, one can just give an image caption, and have it autoregressively generate the image. This is the structure of Google Parti. [34]
Twitter started testing the closed captions toggle, showing up as a little "CC" button on a video with available captions, back in April. Tweet may have been deleted (opens in a new tab) Now, the ...
Re-captioning is used to augment training data, by using a video-to-text model to create detailed captions on videos. [ 6 ] OpenAI trained the model using publicly available videos as well as copyrighted videos licensed for the purpose, but did not reveal the number or the exact source of the videos. [ 4 ]
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