Vision Language Model Prompt Engineering Guide for Image and Video Understanding

Evolution of VLMs: Provides an overview of the evolution of Vision Language Models (VLMs) and their use in image and video understanding.
Single-image Understanding: Explains how a VLM can analyze, describe, classify, and reason over a single image. It can also be used to detect basic events in a livestream by sampling frames.
VLM Response for Single-image Understanding: Demonstrates the VLM's ability to accurately respond to prompts and output structured information for downstream tasks, though limited to simple use cases.
Multi-image Understanding: Discusses the use of multi-image input to enhance VLM capabilities, such as estimating stock levels and use in multimodal RAG pipelines.
VLM Response for Multi-image Understanding: Showcases the VLM's improved deduction abilities, especially in scenarios like a worker dropping a box in a warehouse.
Video Understanding: Explores how VLMs with long context and video understanding can process frames over time to analyze actions and events, like detecting if a fire is growing in a video.
Directional Questions in Video Understanding: Highlights how VLMs with video understanding can address directional questions by comprehending actions over time.
Upcoming Webinar: Promotes the "Vision for All: Unlocking Video Analytics with AI Agents" webinar for further insights on VLMs and visual AI agents.