现代计算机视觉AI实战：Vision Transformers与LLM融合 – CG群

本课程全面覆盖Vision Transformers与LLMs融合技术，使用Python、PyTorch、Hugging Face和OpenCV，系统掌握图像识别、目标检测、智能分割及多模态视觉语言模型，助力构建商业级视觉AI应用。

原始标题：Modern Computer Vision AI with Vision Transformers and LLMs

Modern Computer Vision AI with Vision Transformers and LLMs

本课程是一门面向未来多模态发展的**《现代计算机视觉与大模型（Vision AI）全栈实战课》**。课程打破了传统卷积神经网络（CNN）的局限，全面拥抱 Vision Transformers (ViT) 与大语言模型（LLMs）的融合体系。学员将使用 Python、PyTorch、Hugging Face 和 OpenCV，系统掌握从图像识别、实时目标检测（YOLO、DETR、Grounding DINO）到智能图像分割（SAM）的核心技能。

在更前沿的 AI 落地阶段，课程聚焦于多模态视觉语言模型、视频智能与生成式视觉。学员将深入起底 CLIP 跨模态理解、BLIP 视觉问答、TimeSformer 视频流分析，以及扩散模型（Diffusion Models）的图像智能编辑与局部重绘（Inpainting）。通过对前沿大模型全生命周期的全流程追踪，学员能够独立构建具备“视觉推理”能力的商业级多模态 AI 应用，非常适合 AI 研发人员、科研学生及希望跨界大模型视觉的工程师。

Master Recognition, Detection, Segmentation, Vision-Language Models, Reasoning, Video Intelligence and Generative Vision

What you’ll learn
⚡ Master Modern Computer Vision using Vision Transformers and Large Language Models (LLMs)
⚡ Understand the fundamentals of Image Recognition, Object Detection, and Image Segmentation
⚡ Build Computer Vision applications using Python, PyTorch, and OpenCV.
⚡ Learn Vision Transformer (ViT) architecture and self-attention for image classification.
⚡ Perform real-time Object Detection using YOLO, DETR, DINO, Grounding DINO
⚡ Perform Image Segmentation using YOLO Segmentation and Segment Anything Model (SAM)
⚡ Learn Vision-Language Models using CLIP for image-text understanding
⚡ Build AI-powered Image Captioning and Visual Question Answering using BLIP
⚡ Understand Multimodal AI by combining Computer Vision with Large Language Models
⚡ Learn Depth Estimation and Human Pose Estimation for 3D scene understanding
⚡ Analyze videos using TimeSformer and Video Transformers
⚡ Understand Image Generation using modern Generative AI techniques. Learn Diffusion Models for AI-powered Image Editing and Inpainting.
⚡ Understand the latest advancements in Computer Vision, Vision AI, and Generative AI
⚡ Gain practical skills to build end-to-end AI applications using state-of-the-art Vision AI models

Requirements
❗ Basic knowledge of Python programming. Basic understanding of Deep Learning is recommended.
❗ No prior Computer Vision experience is required.
❗ No prior knowledge of Vision Transformers or Large Language Models (LLMs) is required.
❗ Willingness to learn through hands-on coding and practical projects.
❗ Curiosity to explore modern Computer Vision, Vision AI, and Multimodal AI technologies.

Description

Modern Computer Vision AI with Vision Transformers and LLMs
Modern Computer Vision has evolved far beyond traditional image classification. Today, Vision AI systems can recognize objects, detect multiple objects, segment images, understand natural language, reason about visual scenes, analyze videos, generate realistic images, and intelligently edit existing images. These capabilities are transforming industries such as healthcare, autonomous driving, robotics, manufacturing, retail, surveillance, and smart automation.

This course provides a structured learning journey through the world ofModern Computer Vision, Vision Transformers, Vision-Language Models, and Large Language Models (LLMs). Rather than learning individual models in isolation, you’ll understand how they connect together to build intelligent Vision AI systems capable of solving real-world problems.

Your learning journey includes
✨ Building a strong foundation in Modern Computer Vision and Vision AI

✨ Understanding how Computer Vision has evolved from CNNs to Vision Transformers and Foundation Models

✨ Learning the core Computer Vision tasks

✨ Image Recognition

✨ Object Detection

✨ Image Segmentation

✨ Vision-Language Models

✨ Vision Reasoning

✨ Depth & Pose Estimation

✨ Video Intelligence

✨ Image Generation

✨ Image Editing

✨ Exploring state-of-the-art Vision AI models, including

✨ ResNet-50

✨ Vision Transformer (ViT)

✨ YOLO

✨ DETR

✨ DINO

✨ Grounding DINO

✨ Segment Anything Model (SAM)

✨ CLIP

✨ BLIP

✨ TimeSformer

✨ Diffusion Models

✨ Building practical Vision AI applications using Python, PyTorch, Hugging Face Transformers, OpenCV, Ultralytics YOLO, and Streamlit

The course follows a simple and consistent learning approach for every major model
✨ Understand the problem the model solves

✨ Learn the core intuition behind the architecture

✨ Explore how the model works

✨ Implement the model using modern AI frameworks

✨ Apply it to real-world Computer Vision applications

This structured approach helps you develop both conceptual understanding and practical implementation skills, making advanced Computer Vision topics easier to learn, understand, and apply.

By the end of this course, you’ll be able to
✨ Understand the complete Modern Computer Vision and Vision AI ecosystem

✨ Explain how Vision Transformers and Large Language Models (LLMs) are transforming Computer Vision

✨ Apply state-of-the-art Computer Vision and Vision AI models to real-world problems

✨ Build practical AI-powered Computer Vision applications

✨ Develop a strong foundation for advanced Computer Vision, Multimodal AI, and Generative AI systems

Whether you’re building intelligent AI applications, exploring the latest advances in Vision AI, or expanding your expertise in Computer Vision, this course provides a clear, practical, and comprehensive path to mastering the technologies that power the next generation of AI systems.

Who this course is for
⭐ Anyone looking to stay up to date with the latest advancements in Modern Computer Vision, Vision Transformers, and Large Language Models (LLMs).
⭐ Researchers and graduate students working in Computer Vision and AI
⭐ Anyone interested in Vision Transformers and Multimodal AI

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