Скачать или смотреть BLIP Explained: A Unified Vision Language Model

Unlock the power of Vision-Language Models (VLMs) with this complete walkthrough of BLIP—a breakthrough AI model by Salesforce. In this video, we dive deep into how BLIP combines image understanding with natural language generation.

Learn how BLIP’s unified architecture enables image captioning, visual question answering (VQA), and image-text retrieval. We also demonstrate how to implement BLIP using HuggingFace Transformers in Python.

Whether you're into computer vision, deep learning, or multimodal AI, this tutorial is your one-stop guide!

Labellerr’s GitHub: GitHub - Labellerr/Hands-On-Learning-in-Computer-Vision: Hands-On Learning in Computer Vision

🔍 Topics Covered:
What is BLIP and why it matters
Key innovations: MED architecture & CAPFIL filtering
Real use cases of VQA and captioning
Hands-on coding with BLIP from HuggingFace
Model limitations and strengths

🔗 Resources:
Blog: BLIP Explained: Use It For VQA & Captioning
Notebook: Hands-On-Learning-in-Computer-Vision/Model Notebooks/BLIP at main · Labellerr/Hands-On-Learning-in-Computer-Vision

Chapters
[0:00] Introduction to Labeller's SAM2 Tool
[0:12] Uploading a Football Video for Tracking
[0:24] Selecting Objects to Track (Ball and Player)
[0:35] Using the Magic Brush and SAM2 Model
[0:48] Adding Point Prompts for Segmentation
[1:01] Visualizing Tracked Objects in the Timeline
[1:11] Tracking the Ball with SAM2
[1:18] Tracking the Player with SAM2
[1:42] Previewing Seamless Object Tracking
[1:50] Handling Occlusions and Out-of-Frame Objects
[2:09] Conclusion: Easy Object Tracking with SAM2

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