Vision-Language Models: Use Cases
1. Introduction
In today’s rapidly evolving technological landscape, Visual Language Models (VLMs) stand out as a groundbreaking innovation with transformative potential across multiple domains and industries. From healthcare and banking to retail and manufacturing, VLMs are redefining how we interpret and interact with both text and visual data. By seamlessly integrating these two forms of information, VLMs offer unprecedented levels of efficiency, accuracy, and personalization. As we delve into specific use cases, it’s clear that the technology is not just promising but is already delivering tangible benefits. The time to leverage the capabilities of VLMs is now, as they are set to become an integral part of our digital future.
Tech companies like Google and OpenAI have successfully implemented VLMs in various projects. For instance, Google’s use of VLMs in Google Photos for image recognition and tagging has revolutionized how we interact with our digital memories
The practical utility of vision-language models is best demonstrated through real-world case studies.
2. USECASE across Domains
AI has become a powerful force and has taken a big leap in generative AI with LLMs and VLMs. VLMs have become a very powerful and game-changing force in the world of technological progress. The way these models combine visual and large-language data is not just interesting from a scholarly point of view; it also has real-world applications that can help businesses in big ways.
The main goal of this blog is to talk about the different ways VLMs are used in four important industries: healthcare, banking, retail, and manufacturing. These domains are in focus as the maturity level of AI adoption is very high. This could be because they have a lot of resources, but it could also be because their services are very important and could gain a lot from intelligent automation and data analytics. In these businesses, the possible outcomes are important because using VLMs can have a lot of positive effects on society and business benefits.
Virtual learning machines (VLMs) are used in the healthcare field for more than just automating boring but essential tasks like monitoring a patient in a coma for several months. VLMs can help improve the level of care by making it easier to keep a closer eye on patients remotely or with fewer healthcare professionals. Customer service in the banking business is going through a change right now, with lots of Neobanks coming in with faster, more cost-effective, more accurate, and more personalized service.
In the next parts, we’ll look closely at each of these areas to get a full picture of all the different and creative ways VLMs can be used. This study will look at different use cases, such as remote patient monitoring systems in healthcare and automated customer service in banking, to get a full picture of their technical complexities and how they affect business operations.
2.1. HEALTHCARE
Remote Patient Monitoring: LMs can be used in remote patient monitoring systems to analyze visual data like the patient’s appearance or the readings from medical devices displayed on screens. This provides healthcare providers with a more comprehensive understanding of a patient’s condition, allowing for timely interventions. The use of VLMs in remote monitoring systems enhances the quality of care and can be particularly beneficial for managing chronic conditions or post-operative recovery.
Telemedicine Enhancement: VLMs can elevate the telemedicine experience by analyzing both visual and audio cues during a consultation. By interpreting a patient’s facial expressions, body language, and spoken words, VLMs can offer real-time insights to healthcare providers. This enhanced level of interaction and understanding not only improves the quality of remote consultations but also sets a new standard for telemedicine, making it a more interactive, personalized, and effective healthcare solution.
Automated Radiology Reporting: VLMs can be employed to analyze medical images like X-rays or MRIs along with the accompanying textual data, generating preliminary radiology reports. Their capability to understand both text and images allows for a nuanced interpretation of medical data. This accelerates the diagnostic process and enables radiologists to focus on more complex cases, thereby improving overall efficiency and patient throughput in healthcare facilities.
Virtual Health Assistants: VLMs can serve as virtual health assistants, capable of understanding and responding to patient queries that include both text and images. For example, a patient could send a photo of a skin condition along with a text description. The VLM can analyze both to provide preliminary advice or direct the patient to the appropriate specialist. This makes healthcare advice more accessible and immediate, improving patient engagement and satisfaction.
Hospital Workflow Optimization: VLMs can analyze visual data from hospital cameras to optimize workflows. For instance, they can identify bottlenecks in emergency rooms or high-traffic areas and suggest reorganization strategies. This leads to more efficient hospital operations and a better patient experience as wait times and service delivery improve.
Medical Imaging: VLMs can significantly enhance the field of medical imaging by providing insightful preliminary reports on X-rays, MRIs, and other scans. By analyzing both the medical image and accompanying notes, VLMs offer a comprehensive view that speeds up and refines the diagnostic process. This allows radiologists to focus on more complex cases, optimizing healthcare delivery. The integration of VLMs promises to improve diagnostic efficiency and accuracy, particularly in high-volume or urgent care settings.
2.2. BANKING
Customer Service Automation: VLMs can revolutionize customer service in banking by analyzing both text and visual elements like screenshots in customer queries. This dual-mode analysis allows for quicker and more accurate issue resolution. Implementing VLMs enhances customer satisfaction by providing faster and more precise answers while also freeing up human agents to focus on complex issues. This technological edge is particularly crucial for Neobanks, which rely on digital-first strategies to differentiate themselves and attract a tech-savvy customer base.
Queue Management: VLMs can analyze visual data from cameras in bank branches to manage queues more effectively. The system can identify peak times and allocate resources accordingly. With VLMs, even the mood of waiting customers can be assessed to provide immediate service adjustments. This not only optimizes staff allocation but also takes customer service to a new level by reducing wait times and improving the overall quality of in-branch experiences.
Fraud Detection and Prevention: VLMs can analyze visual data from ATM cameras to identify suspicious activities, such as skimming or multiple failed attempts. By learning from a vast array of visual cues, VLMs can even predict new types of fraud before they become widespread. This not only enhances the bank’s security measures but also positions the bank as a leader in technological innovation, thereby increasing customer trust and loyalty.
Customer Onboarding Automation: VLMs can automate the document verification process during customer onboarding. They can analyze images of identification documents and compare them with selfie photos for quick verification. The use of VLMs ensures a level of accuracy that is often superior to human inspection. This not only speeds up the onboarding process but also sets a new standard in the customer experience, reducing manual labor costs and elevating the bank’s reputation for efficiency.
Virtual Financial Advisor: VLMs can be integrated into video conferencing tools to serve as virtual financial advisors. By analyzing both the visual and audio cues during a consultation, the VLM can offer real-time advice or flag important discussion points. The VLM’s ability to understand nuanced visual cues can make the advice more personalized than ever. This not only enhances customer service but also provides a futuristic, personalized financial advisory experience, increasing customer satisfaction and retention.
Real-Time Document Collaboration: VLMs can facilitate real-time collaboration on financial documents. By analyzing the visual elements in shared documents, the VLM can highlight areas that require attention or approval. The VLM’s advanced capabilities can even suggest improvements or corrections in real-time. This not only improves operational efficiency but also ensures a higher level of accuracy, reducing the risk of errors and positioning the bank as a technology innovator.
Other use cases in banking are Credit Risk Assessment, Investment Portfolio Visualization, Branch Design Optimization, Customer Sentiment Analysis, ATM Design Optimization, Marketing Campaign Effectiveness, Secure Document Handling, and Remote Account Management
Credit Risk Assessment employs VLMs to analyze visual data from customer documents, social media, or even video interviews to assess creditworthiness. Investment Portfolio Visualization uses VLMs to create dynamic, easy-to-understand visual representations of complex investment portfolios. Branch Design Optimization leverages VLMs to analyze customer flow and behavior in physical branches, suggesting design improvements. Customer Sentiment Analysis uses VLMs to interpret facial expressions during customer interactions, whether in-branch or via video calls, to gauge customer satisfaction. ATM Design Optimization uses VLMs to analyze how customers interact with ATMs, suggesting design changes for better usability. Marketing Campaign Effectiveness employs VLMs to analyze customer reactions to visual marketing materials, helping to refine future campaigns. Secure Document Handling leverages VLMs to visually scan and authenticate sensitive documents, adding an extra layer of security. Remote Account Management uses VLMs to facilitate the secure and efficient handling of customer accounts through visual verification methods.
2.3. RETAIL
Product Review: A real-time product review can be generated based on customer facial expressions. Retail stores equipped with cameras capture customers’ facial expressions when interacting with new products. The VLM analyzes these expressions to understand emotional responses and generates real-time product reviews based on these expressions. These reviews are generated as text and displayed on digital platforms or in-store displays. Customers can optionally confirm or edit these automated reviews. The system can provide real-time feedback for retailers and authentic reviews for future customers.
Visual Search: Customers can take pictures of products they are interested in, and VLMs can provide similar items available in the store (online and offline). VLMs can enhance online recommendation systems by analyzing the visual elements of products. Unlike collaborative and content-based filtering, VLMS can identify visual similarities in patterns, colors, and styles to offer more accurate suggestions. VLMs can also analyze user-generated content to gauge real-world satisfaction and fit. By understanding the context in which an item is worn, VLMs can make occasion-specific recommendations. In summary, a more personalized, contextual, and visually aware shopping experience is the future.
Optimizing Retail Displays: VLMs can be a game-changer for display management and store settings. Cameras equipped with VLMs capture and analyze images of product displays and customer interactions. Based on this analysis, the VLM generates data-driven recommendations for optimizing product placement, signage, and lighting. Retail staff can implement these suggestions and continuously monitor their effectiveness through the VLM, leading to a more visually appealing and engaging shopping experience for customers.
Other use cases in retail are diverse: automated inventory management, customer behavior analysis, virtual try-ons, trend forecasting, and in-store navigation.
Automated inventory management can use VLMs to scan shelves and alert stores about low stock. Customer Behavior Analysis employs VLMs to study security footage and understand shopper habits. Virtual try-ons can use VLMs to overlay clothes on customer photos for a virtual fitting room experience. Trend forecasting leverages VLMs to analyze popular items and predict fashion trends. In-store navigation uses VLMs to guide customers to products by interpreting visual cues in the store.
2.4. MANUFACTURING
Quality Control Automation: VLMs can be used to automate quality control processes by analyzing images of manufactured products to identify defects or irregularities. Cameras capture images of products on the assembly line, and the VLM analyzes these images to flag any issues. This reduces the need for manual inspection, significantly speeds up the quality control process, and minimizes the risk of defective products reaching the market.
Predictive Maintenance: VLMs can analyze visual data from machinery to predict when a machine is likely to fail. By continuously monitoring the visual condition of machine parts through cameras, the VLM can identify wear and tear or other signs of impending failure. Early detection allows for timely maintenance, reducing downtime and extending the lifespan of machinery, thereby saving costs.
Real-Time Monitoring of the Supply Chain: Cameras equipped with VLMs can monitor and analyze the visual aspects of inventory, such as the condition and quantity of raw materials or finished goods. This enables real-time inventory tracking and ensures that supply chain operations are optimized, reducing storage costs and improving efficiency.
Worker Safety Compliance: VLMs can be used to monitor the manufacturing floor to ensure that safety protocols are being followed. The model can identify if workers are not wearing safety gear or are engaging in unsafe practices. This enhances worker safety and minimizes the risk of accidents, which can lead to costly legal issues and downtime.
Other use cases in manufacturing are varied: Process Optimization, Energy Efficiency Monitoring, Product Customization, Assembly Line Balancing, Material Handling, Waste Reduction, Supplier Quality Assessment, and Remote Troubleshooting
Process optimization employs VLMs to analyze images of manufacturing processes, identifying bottlenecks or inefficiencies. Energy Efficiency Monitoring uses VLMs to visually assess the energy consumption of machines, helping to optimize usage. Product customization can leverage VLMs to interpret customer-submitted designs or preferences visually, streamlining the customization process. Assembly line balancing utilizes VLMs to analyze the flow of materials and products on the assembly line, ensuring optimal productivity. Material handling uses VLMs to visually identify and sort materials, improving the efficiency of automated systems. Waste reduction employs VLMs to analyze waste materials, helping to identify opportunities for recycling or reusing. Supplier Quality Assessment leverages VLMs to visually inspect incoming raw materials, ensuring they meet quality standards. Remote troubleshooting can use VLMs to diagnose machine issues from a distance by analyzing visual data, reducing the need for on-site visits.
2.5. GENERIC across domains
Emergency Response: In disaster management, VLMs can analyze real-time data feeds in the form of images from the field to assist in coordinating emergency responses. This could be effectively used in many unmanned surveillance operations.
Accessibility: VLMs can describe visual content to visually impaired users, making services more accessible.
Multilingual Support: In global operations, VLMs can translate visual and textual content into multiple languages, aiding in communication and documentation. Content Moderation: VLMs can be used to automatically flag or remove inappropriate content in both text and images, making platforms safer and more user-friendly.
Sentiment Analysis: By analyzing visual elements like emojis or images, VLMs can provide a more comprehensive understanding of public sentiment on social media platforms, workplaces, and public places.
Interactive Learning: Educational platforms can use VLMs to create more engaging and interactive learning experiences. For example, a VLM could generate questions based on a video lecture or provide explanations for diagrams.
3. Summary
In summary, vision language models are revolutionizing various industries by offering a plethora of use cases that are both technically advanced and business-centric. In healthcare, VLMs are enhancing remote patient monitoring, telemedicine, and even hospital workflows. Banking is witnessing a transformation in customer service automation, fraud detection, and even in-branch experiences through the use of VLMs. Retailers are leveraging VLMs for real-time product reviews, visual search, and optimizing retail displays. Manufacturing is not far behind, with VLMs ensuring employee safety, automating quality control, and even predicting machinery maintenance needs.
However, the journey from a use case to a full-fledged implementation is not without its challenges. Both business and technical considerations are crucial for taking these applications forward. Factors such as data privacy, cost of implementation, and the readiness of the existing infrastructure are just as important as the technological prowess of the VLMs themselves.
As we move forward, it’s clear that the integration of VLMs into these key sectors is not just an upgrade; it’s a necessity for any organization looking to stay ahead in this competitive landscape. Whether it’s improving patient outcomes, enhancing customer experiences, optimizing retail spaces, or ensuring manufacturing efficiency, VLMs are setting new standards and expectations across the board.
4. Blob Series
This blog is part of a series aiming to provide a comprehensive guide to VLMs, covering their architecture, applications, how to train and fine-tune a VLM, and finally ongoing research and future prospects.
4.1 Published so far
Vision-Language Models: Introduction
Vision-Language Models: Use Cases
4.2 What’s next?
Vision-Language Models: Available VLMs
Business considerations: Why use VLMs?
Technical considerations: What to consider?
Training & fine-tuning your own VLMs
Ongoing Research & Future of VLMs
Stay tuned for actionable insights on implementing vision-language models effectively in your organization or creating your own VLM.
