There’s a growing synergy between Generative AI and Computer Vision (CV) and we couldn’t be happier!

According to a March 2023 survey by Salesforce, 57% of IT managers termed Generative AI as a “game changer.” Generative AI applications are proliferating as innovators try to tap its awesome potential. Many of these applications marry generative AI’s power with other established technologies. A Salesforce survey found that 67% of respondents think Generative AI can help them leverage more out of other technologies.

Generative AI is effective at data and content augmentation – through the creation of new text, images, and videos. At the basic level, this data is valuable for training AI-powered computer vision models. What’s even better is that Generative AI can accurately understand data patterns and trends – and use this understanding to generate new data to train the models.

However, Generative AI promises more value than simply answering user’s questions. In this blog, let’s look at 4 ways in which Generative AI is taking CV applications to the next level.

4 Applications of Generative AI in Computer Vision

Here are 4 ways by which Generative AI is transforming CV applications:

1. Realistic images

Effectively, Generative AI models can transform textual inputs into high-resolution visuals – so essential in many CV use cases. Trained using neural networks, conditional generative models generate realistic images from text descriptions like “a red panda wearing a tuxedo.”

This application effectively blurs the difference between AI and human creativity. In computer vision, Generative AI can now generate “original” images of:

• Physical objects
• Buildings
• Machine parts
• Natural landscapes

2. High-resolution images

Computer vision thrives on having high-quality and high-resolution images for visual clarity. Generative AI models like Swin2SR can easily transform low-resolution into high-resolution images. With this capability, CV can play a critical role in applications like medical imaging, diagnostics, and photography.

Similarly, high-quality images (or camera footage) can elevate CV usage in applications like:

• 24/7 surveillance
• Weapon detection
• PPE monitoring in hazardous facilities
• Inventory management

3. Synthetic data

Generative AI is efficient at generating synthetic data used to train CV models in adverse environmental conditions like low-light areas and extreme weather. For a long time, manual data annotation has restricted data generation and AI adoption. On the other hand, synthetic data is automatically labeled and creates new data elements useful for training high-end CV models.

What’s more, Generative AI-driven synthetic data is not subject to stringent data privacy regulations – as it is not directly linked to a real individual. With synthetic data, CV adopters can also overcome challenges like AI bias.

4. Data resources

For accuracy, computer vision models are trained on massive data volumes. With Generative AI, CV models can now access larger stores of data – including data that was never previously accessed. For instance, CV users can answer specific data-related questions such as:

• Which location was this image captured?
• Which physical objects are present in this footage or image?

Effectively, using image-to-text conversion, Computer vision and Generative AI can provide deeper insights into the available image.

How is Generative AI benefiting computer vision applications?

Generative AI models can augment datasets used to train CV models. Typically, this occurs as a 3-step process namely:

1. Train the Generative AI model with the existing data and dataset.
2. Use the trained Generative AI model to generate new data.
3. Use the newly generated data to augment the existing dataset.

What are the benefits of data augmentation with CV applications? Here are some of them:

• Improved performance – New data from Generative AI models can enhance the training of computer vision for accurate object detection and identification, thus delivering better model performance.
• Accuracy in image segmentation – New Generative AI data is useful for precise image segmentation, thus improving overall accuracy.
• Improved image analysis – Using synthetic images, Generative AI can train computer vision applications (for example, in healthcare) for improved analysis.

Among the primary benefits, Generative AI has an in-built ability to detect and identify objects accurately in any surroundings. Generative AI tools can improve image and video quality by:

• Eliminating all noise and background disturbances from the captured images and videos.
• Enhancing the image resolution for better clarity.

Future of Generative AI in Computer Vision

Over the previous year, Generative AI has captured attention and imagination globally – and will continue to transform the future. McKinsey estimates that Generative AI will add up to $4.4 trillion to the global economy.

Here’s how Generative AI can enhance computer vision in the future:

• Generate more new and realistic data to train CV models for additional use cases.
• Use synthetic data to train CV models in adverse environmental conditions.
• Power new image and video editing tools to modify image styles or automatically remove (or replace) a selected object from the final image.

According to McKinsey, Generative AI will have the maximum impact on industries like:

• High technology
• Banking
• Pharmaceutical
• Education

Going forward, Generative AI in computer vision will power the emergence of smarter and safer cities. Powered by Generative AI, smart city systems can optimize the daily traffic flow, improve public safety, and improve energy consumption. Smart city authorities can also respond faster to emergencies like natural disasters or terrorist attacks.

Conclusion

With the coming of Generative AI, computer vision systems can be more efficient, productive, and useful across industry use cases. Generative AI provides a stream of high-quality visual data – necessary for the advancement of computer vision applications.

At KamerAI, we simply don’t “talk” about the potential of computer vision – but “actionize” this technology for the benefit of our customers. Here are some demo videos where you can see AI-powered computer vision in action.

Do you want to explore how Generative AI can take computer vision to the next level? Speak to our CV experts.