Prompting Vision Language Models

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Prompting Vision Language Models

The article provides a comprehensive guide to prompting techniques for Vision Language Models (VLMs), covering zero-shot, few-shot, chain-of-thought, and object detection guided prompting approaches.

It includes detailed implementations, code examples, and practical demonstrations using OpenAI's GPT-4o-mini model, showing how different prompting strategies affect model outputs.

Reasons to Read -- Learn:

practical implementations of four different VLM prompting techniques with complete code examples and helper functions, enabling you to effectively work with vision-language models in real applications.

how to combine object detection models with VLMs to enhance image understanding capabilities, including a detailed implementation using the OWL-ViT model for open-vocabulary detection.

how different prompting strategies affect VLM outputs, with concrete examples showing how few-shot examples influence caption length and style, and how chain-of-thought prompting enables better reasoning.

17 min readauthor: Anand Subramanian

OpenAI GPT-4oOpenAIOWL-ViT

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