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  2. Image stitching - Wikipedia

    en.wikipedia.org/wiki/Image_stitching

    Two images stitched together. The photo on the right is distorted slightly so that it matches up with the one on the left. Image stitching or photo stitching is the process of combining multiple photographic images with overlapping fields of view to produce a segmented panorama or high-resolution image.

  3. DALL-E - Wikipedia

    en.wikipedia.org/wiki/DALL-E

    DALL-E, DALL-E 2, and DALL-E 3 (stylised DALL·E, and pronounced DOLL-E) are text-to-image models developed by OpenAI using deep learning methodologies to generate digital images from natural language descriptions known as prompts. The first version of DALL-E was announced in January 2021. In the following year, its successor DALL-E 2 was released.

  4. Text-to-image model - Wikipedia

    en.wikipedia.org/wiki/Text-to-image_model

    A successor capable of generating more complex and realistic images, DALL-E 2, was unveiled in April 2022, [11] followed by Stable Diffusion that was publicly released in August 2022. [12] In August 2022, text-to-image personalization allows to teach the model a new concept using a small set of images of a new object that was not included in ...

  5. Google Photos’ new AI-powered feature turns your 2D snaps ...

    www.aol.com/google-photos-ai-powered-feature...

    Google Photos is getting a new feature that gives your snaps a moving 3D makeover. The “Cinematics photos” will be added to the service’s Memories collection of your old images and videos.

  6. Hugin (software) - Wikipedia

    en.wikipedia.org/wiki/Hugin_(software)

    combine overlapping images for panoramic photography; correct complete panorama images, e.g. those that are "wavy" due to a badly levelled panoramic camera; stitch large mosaics of images and photos, e.g. of long walls or large microscopy samples; find control points and optimize parameters with the help of software assistants/wizards

  7. Multimodal learning - Wikipedia

    en.wikipedia.org/wiki/Multimodal_learning

    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 ...