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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.
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.
Similarly, an image model prompted with the text "a photo of a CEO" might disproportionately generate images of white male CEOs, [128] if trained on a racially biased data set. A number of methods for mitigating bias have been attempted, such as altering input prompts [ 129 ] and reweighting training data.
Stable Diffusion was trained on pairs of images and captions taken from LAION-5B, a publicly available dataset derived from Common Crawl data scraped from the web, where 5 billion image-text pairs were classified based on language and filtered into separate datasets by resolution, a predicted likelihood of containing a watermark, and predicted ...
Multimodal learning is a type of deep learning that integrates and processes multiple types of data, referred to as modalities, such as text, audio, images, or video.This integration allows for a more holistic understanding of complex data, improving model performance in tasks like visual question answering, cross-modal retrieval, [1] text-to-image generation, [2] aesthetic ranking, [3] and ...
idiff - compare two images, print information on how much they differ; iinfo - prints basic (width and height of the image and its color depth) or detailed information about the given image; igrep - searches images for matching metadata; iv - a simple image viewer; maketx - a mipmap generation tool
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. [33]