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Computer Vision Annotation Tool (CVAT) is an 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.
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.
CLIP is a separate model based on contrastive learning that was trained on 400 million pairs of images with text captions scraped from the Internet. 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 answer) is most ...
Show a caption if specified. Float the image on the right unless overridden with the location attribute. With an operand, e.g. thumb=Example.png, the operand names an image that is used as the thumbnail, ignoring any size specification. frame Preserve the original image size, and put a box around the image. Show any caption below the image.
37.5 million image-text examples with 11.5 million unique images across 108 Wikipedia languages. 11,500,000 image, caption Pretraining, image captioning 2021 [11] Srinivasan e al, Google Research Visual Genome Images and their description 108,000 images, text Image captioning 2016 [12] R. Krishna et al. Berkeley 3-D Object Dataset
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.
In April 2009 OpenImageIO was accepted into the Google Summer of Code program with four student slots. September 2009 marked the release of Cloudy with a Chance of Meatballs , the first full-length feature film in whose production OpenImageIO , alongside OpenShadingLanguage , has been used as the texturing engine.
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