Image Recognition Support

What is VLM (Vision Language Model)?

Vision Language Model (VLM) is an advanced artificial intelligence model that can understand image content and combine image information with textual information. Unlike traditional image processing technologies, VLM doesn't just "see" images, but can "understand" objects, scenes, relationships within images, and generate text, answer questions, or execute related commands based on image content.

The core capability of VLM lies in its multimodal processing ability - simultaneously processing and understanding information from different sources (visual and textual). This enables VLM 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 and non-textual content of images.

Characteristic	Traditional OCR	VLM (Vision Language Model)
Main Function	Extract text from images	Understand image content and combine with textual information
Information Processing	Single modal (only processes text pixels)	Multimodal (processes both visual information and text semantics)
Understanding Level	Character-level recognition	Scene understanding, object recognition, relationship inference, context awareness
Main Tasks	Document scanning, text extraction	Image description generation, visual QA (VQA), image retrieval, object detection etc.
Context Awareness	Limited, mainly relies on language models in post-processing	Strong, can understand overall context and details of images
Non-text Content Processing	Usually ignored or unable to process	Can identify and understand objects, scenes, actions etc. in images

VLM's advantages include:

• Deeper Understanding: VLM not only reads text but understands semantic content of images, such as recognizing objects, analyzing scenes, understanding relationships between elements in images.

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

• Content Generation: VLM can generate descriptive text for images (Image Captioning).

• Versatility: Beyond text-related tasks, VLM can be applied to broader visual understanding tasks.

Real VLM Application Cases

VLM's powerful capabilities enable wide applications across multiple fields:

1. Visual Question Answering (VQA)

   • Application: Users upload an image and ask questions about its content, like "What color clothes is the person wearing?" or "Was this photo taken indoors or outdoors?"

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

2. Image Captioning

   • Application: Automatically generate concise, accurate text descriptions for images.

   • Scenarios: Automated image annotation, content management systems, social media content generation, assisting visually impaired people understand images.

3. Content-Oriented Image Retrieval

   • Application: Allow users to search images using natural language descriptions, e.g., "Find images of people having meetings in offices"

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

4. Multimodal Data Analysis

   • Application: Combine medical images with patient records to assist diagnosis; analyze product images and user reviews for market trend prediction.

   • Scenarios: Healthcare, retail, finance.

5. Human-Machine Interaction

   • Application: Enable robots or virtual assistants to understand their visual environment and interact more naturally with humans.

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

MaiAgent Image Recognition Advantages

MaiAgent RAG integrates VLM technology to provide technical personnel and developers with convenient and efficient image recognition and understanding solutions, with the following advantages:

Attachment Image VLM Recognition (Image Understanding and Q&A)

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

VLM Recognition of Embedded Images in Attachment Documents

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

Content Q&A for Document Images

Whether for attachment images or embedded document images, MaiAgent RAG supports content-based Q&A, allowing users to ask specific questions about image details and receive accurate answers.

Content Q&A for Knowledge Base Document Images

MaiAgent RAG supports image Q&A in knowledge base documents (requires Prompt under role instructions, displaying images in Markdown format).

Post-VLM Recognition Q&A for Knowledge Base Document Images

MaiAgent introduces an experimental feature that first performs VLM recognition on images in knowledge base documents, then combines recognition results for in-depth Q&A. This further bridges image information with knowledge base connections, providing more comprehensive knowledge services.

Through MaiAgent's VLM technology, you can more deeply mine the value of image information, achieving more intelligent human-machine interaction and automated processes.