The Global Vision Transformers Market size is expected to reach $2.1 billion by 2030, rising at a market growth of 36.5% CAGR during the forecast period.
Image captioning enriches the user experience across various industries, including e-commerce, social media, news, and entertainment. By providing meaningful and contextually relevant captions for images, ViTs improves user engagement and understanding. Therefore, the image captioning segment will capture 15.8% share in the market by 2030. Image captioning can generate personalized captions tailored to individual user preferences, creating a more engaging and interactive user experience. Image captions enhance the accuracy of visual search by associating keywords and context with images. This is particularly valuable in e-commerce, where consumers search for specific products. Some of the factors impacting the market are growing superior performance in computer vision, increasing adoption of transfer learning and pre-trained models, and high installation cost of these.
Vision transformers have demonstrated superior performance in various computer vision tasks, including object detection, image classification, and segmentation. Their ability to capture long-range dependencies and handle complex visual data sets them apart from traditional computer vision approaches, attracting interest from various industries. These are known for superior accuracy and precision in tasks like image classification, object detection, and image segmentation. Additionally, the availability of pre-trained vision transformer models, like ViT, DeiT, and swin transformer, makes it easier for developers to leverage these models for specific tasks. This accelerates the development of applications and reduces the time and resources required for model training. Pre-trained models are a starting point for many developers and organizations. Increasing adoption of transfer learning and pre-trained models has been a pivotal factor in driving the growth of the market.
However, training large ViT models, particularly for complex tasks, consumes a significant number of computational resources and time. Acquiring and sustaining these resources can be prohibitively expensive for businesses with low resources. Building and maintaining ViT models requires a skilled workforce with expertise in machine learning and deep learning. Hiring and training employees in this field can be costly and time-consuming. Deploying ViTs on edge devices, such as smartphones or IoT devices, may require additional investment in optimization to ensure efficient use of resources, which can be costly. High installation cost of these hinders the market’s growth.
The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include Amazon Web Services, Inc. (Amazon.com, Inc.), NVIDIA Corporation, Google LLC (Alphabet Inc.), OpenAI, L.L.C., Synopsys, Inc., Microsoft Corporation, Qualcomm Incorporated, Intel Corporation, LeewayHertz, and Clarifai, Inc.
Image captioning enriches the user experience across various industries, including e-commerce, social media, news, and entertainment. By providing meaningful and contextually relevant captions for images, ViTs improves user engagement and understanding. Therefore, the image captioning segment will capture 15.8% share in the market by 2030. Image captioning can generate personalized captions tailored to individual user preferences, creating a more engaging and interactive user experience. Image captions enhance the accuracy of visual search by associating keywords and context with images. This is particularly valuable in e-commerce, where consumers search for specific products. Some of the factors impacting the market are growing superior performance in computer vision, increasing adoption of transfer learning and pre-trained models, and high installation cost of these.
Vision transformers have demonstrated superior performance in various computer vision tasks, including object detection, image classification, and segmentation. Their ability to capture long-range dependencies and handle complex visual data sets them apart from traditional computer vision approaches, attracting interest from various industries. These are known for superior accuracy and precision in tasks like image classification, object detection, and image segmentation. Additionally, the availability of pre-trained vision transformer models, like ViT, DeiT, and swin transformer, makes it easier for developers to leverage these models for specific tasks. This accelerates the development of applications and reduces the time and resources required for model training. Pre-trained models are a starting point for many developers and organizations. Increasing adoption of transfer learning and pre-trained models has been a pivotal factor in driving the growth of the market.
However, training large ViT models, particularly for complex tasks, consumes a significant number of computational resources and time. Acquiring and sustaining these resources can be prohibitively expensive for businesses with low resources. Building and maintaining ViT models requires a skilled workforce with expertise in machine learning and deep learning. Hiring and training employees in this field can be costly and time-consuming. Deploying ViTs on edge devices, such as smartphones or IoT devices, may require additional investment in optimization to ensure efficient use of resources, which can be costly. High installation cost of these hinders the market’s growth.
Solution Outlook
Under solution type, the market is categorized into hardware and software. In 2022, the software segment witnessed the largest revenue share in the market. ViT software includes deep learning frameworks like TensorFlow, PyTorch, and Hugging Face Transformers, which offer pre-built ViT models and tools for model development. These frameworks streamline creating, training, and fine-tuning ViT models for specific tasks. ViT software provides tools for data preprocessing and augmentation, enabling the cleaning, transformation, and augmentation of image datasets to enhance model training and robustness.Vertical Outlook
On the basis of vertical, the market is divided into retail & eCommerce, media & entertainment, automotive, government, healthcare & life sciences, and others. The automotive segment recorded a remarkable revenue share in the market in 2022. ViTs identify and recognize objects on the road, including vehicles, pedestrians, cyclists, and road signs. This information is vital for making decisions and ensuring safe driving. ViTs are essential for autonomous vehicles to perceive and understand their environment. They help with object detection, path planning, obstacle avoidance, and enabling autonomous driving.Component Outlook
By component, the market is bifurcated into solution and professional services. In 2022, the solution segment held the highest revenue share in the market. ViT solutions make it easier for organizations to adopt ViTs by providing pre-built models, development frameworks, and libraries that streamline the development process. This accessibility encourages more businesses to explore the potential of ViTs. Solutions provide the flexibility to customize ViT models to suit specific applications and industries. This adaptability broadens the scope of ViTs and fosters their adoption in diverse sectors.Application Outlook
Based on application, the market is classified into image classification, image captioning, image segmentation, object detection, and others. In 2022, the object detection segment dominated the market with maximum revenue share. Object detection is essential for autonomous vehicles to identify and track objects such as pedestrians, vehicles, traffic signs, and obstacles. ViTs enhance object detection accuracy and robustness in self-driving cars. Object detection is used in surveillance systems to identify intruders, suspicious activities, and unauthorized objects. ViTs with object detection capabilities improve security and threat detection.Regional Outlook
Region-wise, the market is analyzed across North America, Europe, Asia Pacific, and LAMEA. In 2022, the North America region led the market by generating the highest revenue share. North America, particularly the United States and Canada, are hubs for autonomous vehicles development. The North American healthcare sector benefits from ViTs' capabilities in interpreting complex medical images such as X-rays, CT scans, and MRIs. ViTs have transformed the retail and e-commerce landscape in North America by enabling visual search, personalized product recommendations, inventory management, and automated checkout systems, all of which enhance the shopping experience and operational efficiency.The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include Amazon Web Services, Inc. (Amazon.com, Inc.), NVIDIA Corporation, Google LLC (Alphabet Inc.), OpenAI, L.L.C., Synopsys, Inc., Microsoft Corporation, Qualcomm Incorporated, Intel Corporation, LeewayHertz, and Clarifai, Inc.
Strategies deployed in the Market
Partnerships, Collaborations & Agreements:
- Aug-2023: NVIDIA Corporation came into a partnership with Hugging Face, Inc., a machine learning (ML) and data science platform. Under this partnership, NVIDIA DGX Cloud AI supercomputing was integrated with the Hugging Face platform. Additionally, the partnership helped in the adoption of generative AI through LLMs and customized business data for industry-related applications.
- Dec-2022: NVIDIA Corporation formed a partnership with Deutsche Bank AG, a German multinational investment bank and financial services company. Under this partnership, NVIDIA introduced machine learning (ML) and artificial intelligence (AI) in the financial services sector. Additionally, the experience of Deutsche Bank in the financial industry and that of NVIDIA in AI were combined to generate a range of regulatory-compliant AI-powered services.
- Mar-2023: Google LLC formed a partnership with Replit, Inc., an online integrated development environment. Through this partnership, the developers of Replit got to access Google Cloud infrastructure, services, and foundation models through Ghostwriter, while the collaborative code editing platform of Replit was accessed by Google Cloud and Workplace developers. Additionally, the collaboration advanced the creation of generative AI applications and created an open ecosystem for generative AI.
- Oct-2023: Microsoft Corporation came into a partnership with Siemens AG, a German multinational technology company. Under this partnership, the companies introduced the Siemens Industrial Copilot, which is an AI-powered assistant helping in the betterment of human-machine collaboration in manufacturing. Additionally, the partnership assisted in the amalgamation of Siemens Teamcenter software for product lifecycle management with Microsoft Teams to enable the industrial metaverse.
- Jun-2023: Intel Corporation joined hands with Blockade Games Inc., a tech company offering an AI-powered solution. Under this collaboration, the Latent Diffusion Model for 3D (LDM3D) was introduced. The new product uses generative AI to create visual content in three dimensions. Additionally, the LDM3D creates immersive 3D images with a 360-degree view by generating a depth map using the diffusion process.
Product Launches and Product Expansions:
- Mar-2023: Microsoft Corporation launched the Visual ChatGPT, a system that helps in communication with ChatGPT through the use of graphical user interfaces. The Visual ChatGPT makes use of several foundation models, which help in the regulation of user requests involving editing and image generation.
- Jan-2023: Qualcomm Technologies, Inc., a subsidiary of Qualcomm Incorporated, unveiled the Snapdragon Ride Flex SoC to enhance its Snapdragon Digital Chassis product portfolio. The Snapdragon Ride Flex SoC has the features to support mixed-criticality workloads. The new product assists in the regulation of the workings of several heterogeneous compute resources like ADAS, digital cockpit, and AD functions within a single SoC.
Acquisition and Mergers:
- Jun-2022: Synopsys, Inc. completed the acquisition of WhiteHat Security, an application security provider committed to securing digital business. Through this acquisition, Synopsys strengthened its Software-as-a-Service (SaaS) capabilities and dynamic application security testing (DAST) technology. Additionally, Synopsys improved its application security testing products.
Scope of the Study
Market Segments Covered in the Report:
By Component- Solution
- Software
- Hardware
- Professional Services
- Media & Entertainment
- Government
- Automotive
- Retail & Ecommerce
- Healthcare & Lifesciences
- Others
- Object Detection
- Image Classification
- Image Segmentation
- Image Captioning
- Others
- North America
- US
- Canada
- Mexico
- Rest of North America
- Europe
- Germany
- UK
- France
- Russia
- Spain
- Italy
- Rest of Europe
- Asia Pacific
- China
- Japan
- India
- South Korea
- Singapore
- Malaysia
- Rest of Asia Pacific
- LAMEA
- Brazil
- Argentina
- UAE
- Saudi Arabia
- South Africa
- Nigeria
- Rest of LAMEA
Key Market Players
List of Companies Profiled in the Report:
- Amazon Web Services, Inc. (Amazon.com, Inc.)
- NVIDIA Corporation
- Google LLC (Alphabet Inc.)
- OpenAI, L.L.C.
- Synopsys, Inc.
- Microsoft Corporation
- Qualcomm Incorporated
- Intel Corporation
- LeewayHertz
- Clarifai, Inc.
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Table of Contents
Chapter 1. Market Scope & Methodology
Chapter 2. Market at a Glance
Chapter 3. Market Overview
Chapter 5. Global Vision Transformers Market by Component
Chapter 6. Global Vision Transformers Market by Vertical
Chapter 7. Global Vision Transformers Market by Application
Chapter 8. Global Vision Transformers Market by Region
Chapter 9. Company Profiles
Companies Mentioned
- Amazon Web Services, Inc. (Amazon.com, Inc.)
- NVIDIA Corporation
- Google LLC (Alphabet Inc.)
- OpenAI, L.L.C.
- Synopsys, Inc.
- Microsoft Corporation
- Qualcomm Incorporated
- Intel Corporation
- LeewayHertz
- Clarifai, Inc.
Methodology
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