Large Vision Models (LVMs) are deep learning systems designed to process and interpret visual data like images and videos with human-like accuracy. They leverage neural networks, such as Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), or hybrid models, to extract features and interpret patterns from images.
The capabilities of large vision models are further enhanced when combined with generative AI techniques. Incorporating gen AI into workflows with LVMs has revolutionized industries such as healthcare, where AI-generated augmentations improve diagnostic accuracy.
While much attention has been given to the sophistication of model architectures in discussions about large vision models (LVMs), another critical factor is often overlooked: the quality, diversity, and volume of training data.
In this piece, we shift the spotlight to the foundational aspects, i.e., the critical role of training data and exploring why they are indispensable for building truly robust and reliable LVMs.
Domain-Specific Models for Enterprises
Large Vision Models (LVMs) focused on one specific domain can solve proprietary problems enterprises face. Today, enterprises spend much time, money, and effort in training individual models for each vision task even if these tasks fall under the same use case or business domain. With domain-specific LVMs, the goal for companies is to use a limited set of LVMs, for each business domain, to solve many vision-related tasks.
To put it simply, the focus is on the quality of training data, say, precision, and not the volume that is required for training generic LVMs.
Generic LVMs built on internet images are one-size-fits-all. For instance, ImageNet (14 million labeled images) has been instrumental in training early vision models. Having a large proprietary set of images that look different from others, say internet images, LVM might offer a way to unlock considerable value from the data.
Quality vs. Volume in Training Data
When it comes to training domain-specific vision models, quality outweighs volume. Unlike generic large vision models (LVMs), domain-specific LVMs need fewer labeled datasets. This efficiency stems from the model’s targeted training, which allows it to focus on a narrow set of tasks or applications, reducing the need for massive datasets.
Herein the quality of training data also stems if your company is using internet images or proprietary images. While performance can further improve with more data, the key lies in having high-quality, domain-relevant annotations that ensure accurate results even with limited input.
Annotation companies play a critical role in this ecosystem by offering industry-specific labeling at scale. From healthcare to retail and automotive industries, annotation providers curate millions of labeled images. This enables domain-specific models to train faster and with amazing precision, leveraging a balance of quality and volume. They focus on precision annotated datasets to support the development of vision models that deliver robust and reliable performance, even in niche applications.
Domain-specific LVMs need less label data than generic LVMs to achieve comparable results/ performance. It can be built quickly for that particular domain. Training domain-specific LVM on lesser unlabeled images shows a reduction in model errors though performance could be better with larger data.
The amount of labeled data needed to achieve different accuracies matters. Once you have built a domain-specific LVM, you can save time when building a computer vision system for your domain.
LVM and LVLMs
When paired with language models, LVM enables advanced tasks like creating image captions and visual question answering (e.g., OpenAI’s CLIP).
Large Vision-Language Models (LVLMs) can understand pictures and words at the same time. Similar to how large language models process textual data, LVLMs are made to understand and interpret visual information.
For example, if you show the AI model (LVM) a photo of a dog and ask, “What is this?” They can tell you, “That’s a dog!” Or you can ask them, “How many cats are in this picture?” and they’ll count and answer. It is because these models are trained on several images and sentences together.
Over time, they get better at understanding how images and words match. They’re used in things like helping visually challenged people “see” by describing images, answering questions about images/snapshots, or even making art based on what you describe.
Building Gen AI Experiences using LLM and LVM
Creating generative AI experiences involves combining the capabilities of large language models (LLMs) and large vision models (LVMs) to generate multimodal content.
Generative AI-driven approaches to image annotation ensure that LVMs receive diverse and representative data for pairing text and image understanding for applications like medical diagnostics or immersive virtual experiences.
This synergy enhances interactivity, creativity, and accuracy in generative AI applications.
Industry-specific Proprietary Images for AI Development
Annotation companies acquire proprietary datasets for model training to ensure adherence to the ethical and legal implications of AI models. The sources include collaborations with industry clients, partnerships with research institutions, hospital networks, public data repositories, and in some cases, proprietary data collection efforts using controlled environments like drones or cameras.
For industries like healthcare, companies may work with hospitals or labs under strict data-sharing agreements to access anonymized medical images. In sectors like retail or automotive, companies often partner with businesses that provide consent to use their data for model training.
Data Annotation in Regulated Sectors
Adherence to regulations like GDPR (General Data Protection Regulation) positively impacts model development. Encouraging high-quality, consented data for training models is ideal for industries like healthcare, where accuracy and adherence to regulations are critical.
For annotation companies, these frameworks enhance data quality by enforcing rigorous protocols in data handling, storage, and anonymization. As a result, the training datasets provided to clients are not only accurate and representative but also legally compliant, instilling confidence in the AI models built upon them.
Use Case of Large Vision Models in Autonomous Vehicles
Autonomous vehicles are based on perception systems, and LVMs are at the heart of these systems. These systems enable AVs to “see” and understand the world with human-like precision.
Benefits of LVMs in AV
Combining LVM capabilities to direct business benefits is rather appealing to stakeholders with big ROI, for instance, in the autonomous vehicle (AV) industry. Major benefits include:
l LVMs process camera feeds in real-time, identifying objects, pedestrians, vehicles, traffic signs, and lane markings.
l AVs equipped with LVMs achieve higher route efficiency, as they create a dynamic map of the environment that identifies detours or construction zones without human intervention.
l LVMs excel in handling low-light, foggy, or crowded urban settings, ensuring safety under challenging conditions.
l Fleet operators benefit from a significant reduction in accident risks during nighttime deliveries.
l Seamless integration with multi-sensor systems as LVMs complement LIDAR, RADAR, and GPS.
After reading the above benefits, it must be obvious now that the aim of driverless cars is not just about getting from point A to point B but making AVs capable of intelligently and safely navigating the environment.
Moreover, training LVMs for AVs requires annotated datasets. Partnering with a data annotation service ensures high-quality training data, leading to faster AI development.
Conclusion
Language and vision working together harmoniously appears to be the way of the future for AI, with LVMs leading the way. In this regard, annotation companies offer comprehensive services to unlock their full potential.
LVMs can better understand visuals and their meanings, helping build smarter technologies. It’s also important to use them responsibly in addressing bias, privacy, and ethical issues. This will shape how LVMs are used in the future.