The huge business potential inherent in multimodal models that combine text and vision
Large multimodal models that integrate text and vision have transformed deep learning, merging the strengths of natural language processing and huge information content of the image. These advanced systems can understand both visual and textual content, opening up new possibilities for applications across various industries. From enhancing customer service with intelligent chatbots to transforming e-commerce through personalized recommendations and visual search, these models offer businesses unprecedented opportunities to innovate and improve their operations. This article explores the transformative impact and potential to reshape the future of business solutions of multimodal models to connect text and vision.
Examples shown in the article were prepared with GPT-4 Vision.
1. Large Multimodal Models (LMMs)
Traditional neural network models are designed to handle data from a single source. For example, Convolutional Neural Networks (CNNs) are tailored for image data, while Recurrent Neural Networks (RNNs) and Transformers are often used for text processing.
Multimodal models represent a significant advancement in artificial intelligence, merging information from various types of data (modalities) to enable more comprehensive and versatile understanding. These models are designed to process and integrate data from different sources, such as text, images, audio, and video, to produce more context-aware and accurate results. As humans naturally perceive the world through multiple senses, the goal of multimodal models is to mimic this ability in machines, enhancing their performance in complex tasks.
One of the most prominent areas of research and application in multimodal models is the integration of text and computer vision. This intersection enables a range of applications, from generating descriptive captions for images to visual question answering (VQA) and image-text retrieval.
2. Business Applications of Multimodal Models Connecting Text and Computer Vision
Multimodal models that integrate text and computer vision are transforming various business sectors by enabling advanced data analysis and decision-making capabilities. By leveraging these models, businesses can automate complex tasks, enhance operational efficiency, and improve customer experiences. Some of the potential business applications are shown below.
Image Quality Control:
In industries where visual quality is paramount, such as manufacturing, media, and retail, ensuring high standards of image quality is crucial. Multimodal models can be employed to automatically assess and enhance image quality by combining visual analysis with descriptive metadata.
- Quality Assessment: These models can evaluate the sharpness, color accuracy, and overall aesthetic appeal of images by analyzing visual features and comparing them with standard criteria described in text. For example, a model might assess product photos for e-commerce sites, ensuring they meet brand guidelines.
- Automated Correction: Based on the analysis, the system can suggest corrections, such as adjusting brightness, contrast, or cropping, to meet the required standards.
Product Damage Detection:
Detecting product damage is essential for maintaining quality control in various industries, particularly in manufacturing, logistics, and retail. Multimodal models can automate this process by analyzing visual data alongside textual descriptions or specifications.
- Visual Inspection: The model can identify and classify different types of damage, such as scratches, dents, or cracks, by comparing images of products against predefined defect categories. This helps in quickly isolating defective items.
- Textual Description Matching: Combining visual data with textual descriptions (such as product specifications or defect reports), the model can accurately pinpoint discrepancies or damages, facilitating efficient quality assurance processes.
Example below shows use of LMM with text and vision as supervisor for couriers. Application needs to check if delivered package is not damaged to prevent customer`s complaint and saves photo as a proof that package was in good condition at the time of delivery.
Optical Character Recognition (OCR):
OCR technology extracts text from images, transforming visual data into machine-readable formats. Multimodal models enhance OCR capabilities by integrating visual recognition with natural language processing.
- Document Automation: Businesses can use OCR to digitize and categorize large volumes of documents, such as invoices, contracts, or labels. The model can extract text and contextually analyze it, enabling automated data entry, archiving, and retrieval.
- Enhanced Text Recognition: By leveraging multimodal data, such as the layout of a document and associated metadata, the model can improve text recognition accuracy, even in complex scenarios like handwritten notes or multi-language documents.
The example below presents an application based on optical character recognition. The camera automatically takes a photo of the measuring device several times a day and then a predefined red rectangle is drawn on the image. Next, the ready picture is uploaded to LMM with vision and read value is sent for further processing.
Detection of Product Packaging
Ensuring that products are correctly packed before shipping is vital for customer satisfaction and reducing returns. Multimodal models can verify whether products are properly packed by analyzing both visual and textual data.
- Visual Verification: The model can analyze images of packed products, identifying whether all required items are present and correctly positioned according to packing guidelines.
- Textual and Visual Matching: By cross-referencing packing lists or descriptions with visual inspections, the system can confirm the presence and condition of each item, ensuring compliance with packaging standards.
Example below shows an application which checks whether the product has been properly packaged for shipment. LMM with vision follows the steps of provided instruction and returns hints for the packer what must be done to complete the packing.
3. Not only for final application – fast prototyping
Prototyping certain computer vision solutions has become significantly faster and more efficient with the advent of multimodal models. Traditionally, developing and testing computer vision approaches required extensive time and resources, including the preparation of large, labeled datasets and the manual tuning of algorithms. This process was not only labor-intensive but also uncertain, as there was no guarantee that the final model would meet the developer’s expectations. In contrast, multimodal models streamline this process by leveraging pre-trained architectures and integrating diverse data types, such as text and images. This integration allows for more flexible and intuitive design iterations, enabling developers to quickly experiment with and refine their solutions, ultimately accelerating the path from concept to deployment.
Multimodal models with computer vision capabilities can also be used for more complex tasks. In business, it is often necessary to perform several different tasks simultaneously. These tasks are usually very specific, such as checking if produced furniture is correctly assembled. If this needs to be automated, the system must check numerous specific features, for example, whether the upholstery has been applied correctly, all rivets are in place, and the legs are properly screwed on. Humans need just a few rules to know where the rivets should be and what correctly applied upholstery looks like. To train a computer vision model that can’t just figure it out, developers would need thousands of examples for training, and a single model might not cover all of these requirements. LMMs provide a solution to this problem. They can understand, based on descriptions, what “correctly” means, much like humans do. It may be necessary to fine-tune the multimodal model to achieve the required accuracy, but for initial tests, LMMs with computer vision capabilities are invaluable.
4. Good prompt – the key to success
Creating an effective prompt for a multimodal model is crucial for obtaining accurate and relevant responses. The quality of the prompt directly influences the quality of the output, making it essential to construct it carefully. Here are key considerations to keep in mind when crafting a prompt for LMM with text and vision:
- Clarity and Precision: Use clear and unambiguous language. The prompt should be precise, leaving little room for interpretation. Avoid complex or technical jargon unless the AI model is specifically trained to handle such language. It is important to precisely explain what the model should do with the image step by step to get the expected result.
- Context and Background: Providing sufficient context helps the model understand the scope and nature of the request. Include necessary background information that frames the prompt, ensuring the AI can generate a response that is relevant and appropriate to the situation.
- Specificity and Detail: Be specific about what you want the model to do. Clearly outline the desired format, structure, or content of the response. For example, if you want a summary, specify the length and focus. The more detailed the prompt, the more tailored the response will be.
- Open-Ended vs. Close-Ended: Decide whether the answer should be open-ended, allowing for creative or expansive responses, or close-ended, aiming for a specific and concise answer. Open-ended answers are useful for exploring what model can read from the image and generating ideas of potential application, while close-ended answers are better for factual information, specific tasks or more complex system where LLM’s answer is a step in a process circle.
- Guidance and Examples: If the task is complex or nuanced, providing examples can be very helpful. Examples set a standard for the type of response expected and can help the AI understand the nuances of the task. Including guidelines or specific instructions can further clarify the requirements. It is especially important when a task requires custom products’ recognition on the image, which model did not have a chance to see before.
- Consideration of Limitations: Be aware of the model’s limitations, such as knowledge cutoffs or potential biases. Frame the prompt in a way that minimizes these issues, and be explicit if the response requires current information or sensitive topics.
- Relevance and Focus: Ensure that the prompt is focused on the specific task or question at hand. Avoid including extraneous information that could distract the model from generating a relevant response.
By incorporating these elements, you can craft prompts that effectively guide the multimodal model, leading to high-quality, relevant, accurate responses and making full use of the accompanying image.
5. Limitations (at the time of publication)
Even though the possibilities of multimodal models combining text and vision are immense, there are still some limitations that users should be aware of. It is important to note that the limitations listed below exist at the time of publication of this article and may be addressed in the future.
Counting – Counting objects in an image remains a challenge for multimodal models. They often require additional, step-by-step instructions on how to count correctly. Unfortunately, these instructions frequently have to be specific to each example, making it difficult to create stable business applications with multimodal models based on numeracy.
Coordinates in the image – While multimodal models are effective at object detection, they struggle with providing precise positions of objects in an image. The model can return a general position, such as “bottom left corner,” but for exact coordinates, traditional computer vision models are still necessary.
Optical character recognition (OCR) – It is possible to develop business applications based on OCR, but it requires more work and testing to achieve satisfactory results. Multimodal models with vision capabilities often add extra characters or miss some in their outputs. Crafting a suitable prompt is particularly crucial for OCR applications to make them viable for business use.
Summary
Multimodal models that integrate vision with text are being increasingly adopted in business applications. These models can analyze visual data alongside text, offering various possibilities across industries and enabling the automation of complex processes without the need for large datasets and lengthy development times. However, LMMs with vision face limitations that developers should be aware of to fully realize their potential.
If you want to learn more about multimodal models or are considering using them in your business, feel free to contact us. We are AI experts and can help you leverage these technologies effectively.
