Jesse Ellis
February 26, 2025 09:32
Explore the evolution of VLM (Vision Language Models) from a single image analysis to comprehensive video understanding, emphasizing the function in various applications.
Vision Language Models (VLM) has developed rapidly to change the environment of generated AI by integrating large language models (LLM) and visual understanding. The VLM first introduced in 2020 was limited to text and single image input. However, due to the recent development, it is possible to expand its functions, including multiple images and video inputs, so that complex vision languages such as visual question response, caption, search and summary are possible.
VLM accuracy improvement
According to NVIDIA, rapid engineering and model weight tuning can improve VLM accuracy for specific cases. Technologies such as PEFT allow efficient micro -adjustment, but require important data and calculation resources. On the other hand, prompt engineering can improve output quality by adjusting the runtime temporary text input.
Understanding a single image
VLMS is excellent for understanding a single image through identification, classification and reasoning of image content. You can also provide detailed explanations and translate the text within the image. In the case of live streams, the VLM can detect the event by analyzing individual frames, but this method limits the ability to understand temporal epidemiology.
Understanding multiple image
The multi -image function allows VLM to compare and contrast the image, providing an improved context for each domain work. For example, in the sleeve, VLM can estimate the stock level by analyzing the image of the store shelf. Providing additional contexts such as reference images greatly improves the accuracy of these estimates.
Understanding video
Advanced VLM now has video understanding and handles many frames to understand behavior and trends over time. This allows you to handle complex queries for video content, such as identifying movements or ideals in the sequence. Sequential visual understanding captures the progress of the event, while temporal localization technologies such as Lita improve the exact ability of the model when a particular event occurs.
For example, VLM, which analyzes the warehouse video, can identify the operator who drops the box to provide detailed response to the scene and the potential risk.
NVIDIA provides resources and tools for developers to make the most of VLM’s potential. If you are interested in, you can register VLMs in various applications by registering them in a web seminar on a platform like Github and accessing a sample workflow.
For more information about VLMS and applications, visit the NVIDIA blog.
Image Source: Shutter Stock