Image recognition support

What is a VLM (Vision Language Model)?

A Vision Language Model (VLM) is an advanced artificial intelligence model that can understand image content and combine visual information with textual information. Unlike traditional image-processing techniques, a VLM not only “sees” images but also “understands” the objects, scenes, and relationships within them, and can generate text, answer questions, or execute related commands based on image content.

The core capability of VLM lies in its multimodal processing ability, i.e., processing and understanding information from different sources (visual and textual) simultaneously. This enables VLMs to perform more complex and context-aware tasks.

Comparison between VLM and traditional OCR

Traditional Optical Character Recognition (OCR) technology focuses on detecting and extracting text from images but has limitations in understanding the overall semantics of images and non-text content.

Characteristics	Traditional OCR	VLM (Vision Language Model)
Main functions	Extract text from images	Understand image content and combine it with textual information
Information processing	Unimodal (only processes text pixels)	Multimodal (processes visual image information and textual semantics simultaneously)
Level of understanding	Character-level recognition	Scene understanding, object recognition, relationship inference, context awareness
Primary tasks	Document scanning, text extraction	Image captioning, Visual Question Answering (VQA), image retrieval, object detection, etc.
Context awareness	Limited, mainly relies on language models in post-processing	Strong, can understand the overall context and details of an image
Handling non-text content	Usually ignored or cannot be processed	Able to identify and understand objects, scenes, actions, etc. in images

The advantages of VLM are:

• Deeper understanding: VLMs not only read text but also understand the semantic content of images, such as recognizing objects, analyzing scenes, and understanding relationships between elements in an image.

• Interactivity: VLMs can perform Visual Question Answering (VQA), answering user questions based on image content.

• Content generation: VLMs can generate descriptive text for images (Image Captioning).

• Versatility: In addition to text-related tasks, VLMs can also be applied to a broader range of visual understanding tasks.

Practical applications of VLM

The powerful capabilities of VLMs give them broad application prospects across multiple fields:

1. Visual Question Answering (VQA)

   • Applications: Users upload an image and ask questions about the image’s content, such as “What color is the clothing the person in the image is wearing?” or “Was this photo taken indoors or outdoors?”

   • Scenarios: Smart assistants, education, visual impairment assistance.

2. Image captioning (Image Captioning)

   • Applications: Automatically generate concise and accurate textual descriptions for images.

   • Scenarios: Automated image tagging, content management systems, social media content generation, aiding visually impaired users in understanding images.

3. Content-oriented image retrieval

   • Applications: Allow users to search images using natural language descriptions, e.g., “Find images of people having a meeting in an office.”

   • Scenarios: Large image library management, e-commerce product search.

4. Multimodal data analysis

   • Applications: Combine medical images and patient records to assist doctors in diagnosis; analyze product images and user reviews to predict market trends.

   • Scenarios: Healthcare, retail, finance.

5. Human-computer interaction

   • Applications: Enable robots or virtual assistants to understand their visual environment and interact more naturally with people based on that understanding.

   • Scenarios: Smart robots, autonomous vehicles (understanding traffic signs and road conditions).

Advantages of MaiAgent image recognition

MaiAgent RAG integrates VLM technology to provide technicians and developers with a convenient and efficient image recognition and understanding solution, offering the following advantages:

Attachment image VLM recognition (image understanding and Q&A)

MaiAgent RAG can perform VLM recognition on user-uploaded attachment images, providing accurate understanding of image content

VLM recognition of images embedded within attached documents

MaiAgent RAG is actively developing VLM recognition capabilities for images embedded inside attached documents (such as PDFs, Word documents). This means the system can not only understand the text in documents but also parse image content, achieving true multimodal document understanding. This is an advanced feature that many standard RAG systems do not have.

Content Q&A for attachment images

Whether attachment images or images embedded in documents, MaiAgent RAG supports image-based Q&A, allowing users to ask questions about image details directly and receive accurate answers.

Content Q&A for knowledge base document images

MaiAgent RAG supports image Q&A within knowledge base documents (requires prompting under role instructions to display images in Markdown format)

Q&A after VLM recognition of images within knowledge base documents

MaiAgent has launched an experimental feature that first performs VLM recognition on images within knowledge base documents and then combines the recognition results for in-depth Q&A. This will further bridge image information with the knowledge base to provide more comprehensive knowledge services.

Through MaiAgent’s VLM technology, you can more deeply mine the value of image information to achieve smarter human-computer interaction and automated processes.