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Afonso Diela edited this page Sep 17, 2024 · 1 revision

Level 2 - Hero: Large Vision Models (LVMs) from Image Generation, Inpainting, & More ⚡

Dive into advanced topics such as GANs, text-to-image synthesis, and more using state-of-the-art models.

  1. Creative Image Generation with GANs

    • Description: Explore GANs to generate novel images, experiment with different styles, and create artistic works.
    • Key Concepts: GANs, image generation, generative models.
    • Notebook: Open in Colab

  2. Text-to-Image Synthesis with LLMs and Diffusion Models

    • Description: Generate realistic images from text descriptions using a combination of large language models and diffusion models.
    • Key Concepts: Text-to-image, diffusion models, generative models.
    • Notebook: Open in Colab

  3. AI-Powered Image Restoration and Enhancement

    • Description: Enhance or restore images using AI-powered techniques such as super-resolution and deblurring.
    • Key Concepts: Image restoration, super-resolution, deblurring.
    • Notebook: Open in Colab

  4. Style Transfer with GANs and Image Processing

    • Description: Transfer the artistic style of one image onto another using GANs and other deep learning models.
    • Key Concepts: Style transfer, GANs, artistic images.
    • Notebook: Open in Colab

  5. AI-Driven Image Captioning and Storytelling

    • Description: Automatically generate captions and creative stories from images using large language models (LLMs).
    • Key Concepts: Image captioning, storytelling, LLMs.
    • Notebook: Open in Colab

  6. AI-Assisted Image Editing and Manipulation

    • Description: Use AI to automate complex image editing tasks, such as inpainting, removal, or modifications.
    • Key Concepts: Image editing, AI manipulation, inpainting.
    • Notebook: Open in Colab

  7. AI Image Recognition Benchmarks with SOTA Vision Models

    • Description: Benchmark state-of-the-art (SOTA) vision models on various image recognition tasks, including classification, object detection, and more.
    • Key Concepts: Image recognition, SOTA models, benchmarking.
    • Notebook: Open in Colab

Computer Vision Challenge

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