Explore a comprehensive review of BLIP (Bootstrapping Language-Image Pre-training), a groundbreaking framework for unified vision-language understanding and generation. Delve into the intricacies of cross-modal pre-training, examining how BLIP addresses issues like low-quality datasets and limited fine-tuning capabilities. Learn about the model's architecture, data flow, and parameter sharing between modules. Discover the innovative captioning and filtering bootstrapping process, and understand how BLIP achieves state-of-the-art results in various vision-language tasks. Gain insights into its application to video-language tasks and its potential impact on the field of artificial intelligence.
BLIP- Bootstrapping Language-Image Pre-Training for Unified Vision-Language Understanding and Generation
Overview
Syllabus
- Intro
- Sponsor: Zeta Alpha
- Paper Overview
- Vision-Language Pre-Training
- Contributions of the paper
- Model architecture: many parts for many tasks
- How data flows in the model
- Parameter sharing between the modules
- Captioning & Filtering bootstrapping
- Fine-tuning the model for downstream tasks
Taught by
Yannic Kilcher
Related Courses
-
One Model for All the Tasks - BLIP - Author Interview
-
Tiny Text and Vision Models - Fine-Tuning and API Setup
-
Unified-IO: A Unified Model for Vision, Language, and Multi-Modal Tasks
-
VirTex- Learning Visual Representations from Textual Annotations
-
Pre-Training BERT from Scratch - Building, Fine-Tuning, and Running Inference with KERAS NLP
-
Pre-training Mixtral MoE Model with SageMaker HyperPod - Fine-Tuning and Continued Pre-Training
