Vision Language Models: Evolution and Best Practices for Single-Image and Video Understanding
Single-image understanding
Vision language models (VLMs) are evolving at a breakneck speed. In 2020, the first VLMs revolutionized the generative AI landscape by bringing visual understanding to large language models (LLMs) through the use of a vision encoder. These initial VLMs were limited in their abilities, only able to understand text and single image inputs.
Fast-forward a few years, and VLMs are now capable of understanding multi-image and video inputs to perform advanced vision-language tasks such as visual question-answering (VQA), captioning, search, and summarization.
Temporal localization
VLMs incorporating Localization Interpretable Temporal Attention (LITA) or similar temporal localization techniques elevate video understanding by explicitly learning when and where critical events occur.
Conclusion
This post walked through how VLMs have evolved from supporting only single-image input to being capable of complex temporal reasoning on long video inputs. To get started with VLMs, visit build.nvidia.com and try out some of the prompts shown in this post. For technical questions, see the Visual AI Agent forum.
FAQs
Q: What are VLMs?
A: Vision language models (VLMs) are a type of AI model that combines the capabilities of natural language processing (NLP) and computer vision.
Q: What is the difference between single-image and multi-image understanding?
A: Single-image understanding is limited to understanding a single image, while multi-image understanding allows the model to understand multiple images and perform tasks such as image classification, object detection, and image segmentation.
Q: What is temporal localization?
A: Temporal localization is a technique used in VLMs to identify specific moments or events in a video, allowing for more accurate video understanding and analysis.
Q: How do I get started with VLMs?
A: Visit build.nvidia.com and try out some of the prompts shown in this post. For technical questions, see the Visual AI Agent forum.
