Search results
Results from the WOW.Com Content Network
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.
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.
If the user needs to add annotations, highlightings or obfuscations to the screenshot the built-in image editor can be used. Greenshot's image editor is a basic vector graphics editor; however, it offers some pixel-based filters. It allows to draw basic shapes (rectangles, ellipses, lines, arrows and freehand) and add text to a screenshot.
Learn how to download and install or uninstall the Desktop Gold software and if your computer meets the system requirements.
Job Access With Speech (JAWS) is a computer screen reader program for Microsoft Windows that allows blind and visually impaired users to read the screen either with a text-to-speech output or by a refreshable Braille display. JAWS is produced by the Blind and Low Vision Group of Freedom Scientific.
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 text-to-image retrieval, users input descriptive text, and CLIP retrieves images with matching embeddings. In image-to-text retrieval, images are used to find related text content. CLIP’s ability to connect visual and textual data has found applications in multimedia search, content discovery, and recommendation systems. [31] [32]