Enhancing Vision AI with LLMs

Table of Contents:

1. Introduction
2. Vision AI in Azure Cognitive Services
   • The Power of Vision AI
   • Open-World Recognition
3. Vision-Language Tasks
   • Automatic Image Classification
   • Object Detection
   • Image Segmentation
4. Vision AI for Video Content
   • Frame Analysis
   • Video Summarization
5. Customizing Vision AI Models
   • Few-Shot Learning
   • Custom Model Training
6. Using Vision AI in Apps and Services
7. Case Study: Seeing AI
8. Code Sample: Calling the Azure Vision Service
9. Conclusion
10. FAQs

Article:

Combining Vision and Language with Vision AI in Azure Cognitive Services

Imagine a world where AI models can not only see but also understand and describe visual content using natural language. With the latest vision AI model in Azure Cognitive Services, this is now a reality. In this article, we will explore the capabilities of vision AI and how it combines vision and language to perform various tasks, from automatic image classification to video summarization. We will also discuss the power of open-world recognition and how it allows the model to accurately recognize objects and scenes in a wide range of situations and contexts.

Vision AI in Azure Cognitive Services

Vision AI in Azure Cognitive Services is a powerful model that combines both natural language and computer vision. It is part of the suite of pre-trained AI capabilities offered by Azure Cognitive Services and can perform a variety of vision-language tasks such as automatic image classification, object detection, and image segmentation.

The Power of Vision AI

What sets Vision AI apart is its ability to visually process information within a wide range of situations, scenes, and contexts. Similar to how humans process visual information, Vision AI can accurately recognize objects and scenes by leveraging its large language model and the Project Florence technology in Microsoft's next-generation AI for visual recognition.

Open-World Recognition

Unlike traditional "closed world" training methods that rely on limited sets of meticulously labeled objects and scenes, Vision AI employs open-world recognition. This approach involves training the model across billions of images and millions of object categories, allowing it to accurately recognize objects and scenes in diverse situations and contexts. By combining vision and language, the model can retrieve images and provide comprehensive descriptions even without metadata or GPS information associated with the images.

Vision-Language Tasks

One of the key strengths of Vision AI is its ability to perform various vision-language tasks.

Automatic Image Classification

Vision AI can automatically classify images Based on their content. By analyzing visual features and leveraging its language model, the model can accurately determine the category of an image without the need for metadata or manual labeling.

Object Detection

Object detection is another task that Vision AI excels at. By analyzing images, the model can identify and locate specific objects within the image. This capability can be invaluable in applications that require accurate object detection, such as autonomous vehicles or surveillance systems.

Image Segmentation

Image segmentation is the process of dividing an image into multiple segments, each representing a distinct object or region. Vision AI can perform image segmentation and provide detailed information about different areas of interest within an image. This can be particularly useful in applications that require a deeper understanding of image content, such as medical imaging or visual inspection.

Vision AI for Video Content

Vision AI's capabilities extend beyond static images and into the realm of video content.

Frame Analysis

When applied to video content, Vision AI can perform frame analysis, which involves searching for specific objects or traits in individual frames of a video. By analyzing each frame, the model can locate and identify objects or scenes as requested by the user. This allows for more precise and targeted video analysis and retrieval.

Video Summarization

Another powerful capability of Vision AI is video summarization. By analyzing the content of a video, the model can generate a concise summary that highlights the key elements and events. This can be helpful in scenarios where time is limited, such as reviewing security camera footage or analyzing sports matches.

Customizing Vision AI Models

While Vision AI offers impressive out-of-the-box capabilities, it can also be customized to better suit specific requirements.

Few-Shot Learning

With few-shot learning, developers can provide additional training data and Context to guide the model's understanding. This approach allows for easier customization and fine-tuning of the model without the need for extensive amounts of data.

Custom Model Training

Azure Vision Studio provides a user-friendly interface for training custom models. Developers can import their custom datasets, label the images, and train the model to detect specific objects or scenes. The training process produces a report with metrics that help evaluate the model's accuracy.

Using Vision AI in Apps and Services

The power of Vision AI can be harnessed in various applications and services to enhance accessibility and automate processes.

Vision AI's image and video recognition capabilities can be integrated into apps and services to improve accessibility by automatically generating alt text for images. By providing detailed descriptions, Vision AI enables individuals with visual impairments to better understand visual content.

Case Study: Seeing AI

One noteworthy application of Vision AI is the Seeing AI mobile app. Designed to assist individuals with low vision, Seeing AI uses Azure's Florence-enhanced Vision service. The app leverages Vision AI's capabilities to narrate the world by describing objects and scenes captured by the device's camera. The app can infer the objects it "sees" and audibly describe them to the user, making it easier for individuals with visual impairments to navigate and Interact with their surroundings.

Code Sample: Calling the Azure Vision Service

Integrating Vision AI into your own applications is straightforward. Using a Computer Vision instance, you need to provide the key and the endpoint URL. Then, you select your image file, specify the desired options (such as language and output format), invoke the analyze method, and retrieve the results. With just a few lines of code, you can unlock the power of Vision AI in your own projects.

Conclusion

Combining vision and language in AI models opens up a world of possibilities. With Vision AI in Azure Cognitive Services, developers can leverage the power of computer vision and natural language processing to build applications that understand and describe visual content. From automatic image classification to video analysis, Vision AI offers a wide range of capabilities that can enhance accessibility, improve automation, and deliver immersive user experiences.

To learn more about Vision AI and its capabilities, visit aka.ms/CognitiveVision.

FAQs

Q: What is Vision AI in Azure Cognitive Services? A: Vision AI is a powerful AI model that combines computer vision and natural language processing. It is part of Azure Cognitive Services and offers a wide range of image and video recognition capabilities.

Q: How does Vision AI recognize objects and scenes without metadata? A: Vision AI leverages open-world recognition, which involves training the model across billions of images and millions of object categories. This enables the model to accurately recognize objects and scenes in diverse situations and contexts.

Q: Can Vision AI be customized? A: Yes, Vision AI can be customized using few-shot learning. Developers can provide additional training data and context to guide the model's understanding.

Q: What are some applications of Vision AI? A: Vision AI can be used in various applications, such as automatic image classification, object detection, video summarization, and accessibility features like generating alt text for images.

Q: How can I integrate Vision AI into my own app? A: Integrating Vision AI into your own app is straightforward. You can use Azure Vision Studio to train a custom model or leverage the pre-trained capabilities provided by Azure Cognitive Services. You will need to use the appropriate API endpoint and key in your code to call the Vision AI service.

Q: Does Vision AI support real-time video analysis? A: Yes, Vision AI can perform real-time video analysis by analyzing individual frames of the video. This enables the model to locate and identify specific objects or scenes as required.