Get in Touch

Course Outline

Introduction to Vision-Language Models

  • Overview of VLMs and their function in multimodal AI.
  • Key architectures: CLIP, Flamingo, BLIP, and others.
  • Practical applications: search, captioning, autonomous systems, and content analysis.

Setting Up the Fine-Tuning Environment

  • Configuration of OpenCLIP and related VLM libraries.
  • Standard formats for image-text pair datasets.
  • Preprocessing workflows for visual and linguistic inputs.

Fine-Tuning CLIP and Analogous Models

  • Utilizing contrastive loss and joint embedding spaces.
  • Practical exercise: adapting CLIP to proprietary datasets.
  • Managing domain-specific and multilingual content.

Advanced Optimization Techniques

  • Leveraging LoRA and adapter-based methods for improved efficiency.
  • Implementing prompt tuning and visual prompt injection.
  • Comparing zero-shot versus fine-tuned evaluation outcomes.

Assessment and Benchmarking

  • Key metrics for VLMs: retrieval precision, BLEU, CIDEr, and recall.
  • Diagnostics for visual-text alignment.
  • Visualization of embedding spaces and error patterns.

Deployment and Practical Application

  • Model export for inference using TorchScript or ONNX.
  • Integration of VLMs into pipelines and APIs.
  • Resource management and model scaling strategies.

Case Studies and Real-World Scenarios

  • Media analysis and content moderation workflows.
  • Search and retrieval systems in e-commerce and digital libraries.
  • Multimodal interactions in robotics and autonomous platforms.

Recap and Future Directions

Requirements

  • Foundational knowledge of deep learning in both vision and NLP.
  • Practical experience with PyTorch and transformer-based architectures.
  • Working familiarity with multimodal model structures.

Target Audience

  • Computer vision engineers.
  • AI developers.
 14 Hours

Number of participants


Price per participant

Upcoming Courses

Related Categories