Market Overview: Expanding Use Cases for Large Vision Models
The field of Computer Vision (CV) continues to evolve rapidly, with Large Vision Models (LVMs) and Vision Language Models (VLMs) providing new possibilities for industries looking to adopt more advanced capabilities. ABI Research anticipates that LVMs, including multi-modal variants that combine vision and language processing, will unlock more complex use cases across verticals such as entertainment, automotive, marketing, retail, and manufacturing.
China is at the forefront of LVM adoption, as the country is rapidly developing its autonomous vehicle and robotics markets, each of which benefits tremendously from AI-based vision capabilities.
Chart 1: Regional Breakdown of LVM Software Spending, 2024 to 2030
(Source: ABI Research)
The latest advancements in LVMs and VLMs come as enterprises seek deeper and more accurate insights into visual data and aim to enhance their decision-making processes through AI-driven solutions. While LVMs hold great potential, the shift toward their widespread adoption will come with a set of technological challenges that must be addressed.
“Although plenty of commercial opportunities remain in the computer vision market, the technology is starting to hit its use case limitations, making it vital for vendors to explore LVMs in the enterprise space.” – Reece Hayden, Principal Analyst at ABI Research
Overcoming Cost, Power, and Latency Challenges
One of the primary hurdles in scaling large visual models is their considerable resource demand. LVMs require substantial computing power, energy, and cooling capabilities, which presents challenges for enterprises trying to deploy them in real-time, mission-critical environments.
These demands often translate into high operational costs, power consumption, and latency, all of which make it difficult to implement LVMs effectively. To address these issues, ABI Research recommends focusing on the development of domain-specific, lightweight LVMs tailored to specific use cases.
Neural Architectural Search (NAS) tools, such as those developed by Deci.ai (now acquired by NVIDIA), can automate the creation of more efficient models, reducing the complexity of LVM deployment. Integrating techniques like model merging and spectrum optimization, as explored by companies like Arcee AI, will also be key in minimizing the resource intensity of these models.
Solving Data Availability Issues for Large Visual Models
Data availability is another pressing concern for the successful implementation of LVMs. Unlike traditional CV models that can work with smaller datasets, LVMs need vast amounts of structured data to be trained effectively. However, acquiring high-quality, real-world labeled data can be challenging, especially in industries with unique or niche requirements.
To overcome this barrier, ABI Research suggests leveraging synthetic data generation techniques, such as digital twins, to simulate the behavior of physical assets or systems. These virtual replicas can generate a large volume of synthetic data that mimics real-world scenarios.
However, while synthetic data can help alleviate some of the data scarcity issues, it often falls short of capturing the nuances of actual real-world data. By combining real-world expert-labeled data with synthetic data, companies can improve the accuracy and performance of LVMs.
Furthermore, supporting multi-modal data, which incorporates visual, textual, and sensory data, will further enhance model capabilities and provide a more holistic approach to training LVMs.
Advancing the Role of Multi-Modal LVMs
As LVMs evolve, integrating vision and language processing has become a critical advancement. Multi-modal LVMs combine the visual recognition capabilities of LVMs with the textual processing power of Large Language Models (LLMs), opening the door for dynamic and complex applications.
Static VLMs that analyze images and generate text responses are already in use, but the potential for more advanced use cases is vast. These include image captioning in industrial settings, visual question answering for troubleshooting, and autonomous vehicle driving assistance for understanding Advanced Driver Assistance Systems (ADAS) decisions. In healthcare, VLMs are being used to analyze medical images and provide diagnostic feedback.
Incorporating these additional capabilities brings new challenges in terms of model integration and the need for significant computational resources. However, the potential of multi-modal LVMs to revolutionize industries and deliver more actionable insights is undeniable, making them a crucial area of focus for enterprises investing in AI-driven solutions.
Key Companies
Notable vendors in the large visual model market include:
- Roboflow
- Snorkle AI
- LandingAI
- Edge Impulse
Next Steps
LVMs and Vision VLMs represent the next step in the evolution of CV technology, offering enterprises a range of new opportunities to unlock value from visual and textual data. While these AI visual technologies hold immense promise, overcoming the challenges related to cost, power consumption, data availability, and model integration will be critical for widespread adoption.
ABI Research emphasizes the importance of investing in domain-specific, lightweight models, improving data integration techniques, and advancing multi-modal capabilities to enable enterprises to deploy these powerful models effectively. With the right investments and advancements, LVMs and VLMs can reshape how industries leverage visual data, enhancing decision-making and operational efficiency across sectors.
To learn more about the opportunities, challenges, and market forecasts for LVMs and VLMs, download ABI Research's comprehensive report: The Transition from Computer Vision to Large Visual Models (LVMs): What Are the Implications for Enterprises and Vendors?
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