QVQ-Max - A visual reasoning model launched by Alitongyi

What is QVQ-Max?

QVQ-Max is a visual reasoning model launched by Alibaba Tongyi, and is an official upgrade of QVQ-72B-Preview. QVQ-Max can "understand" image and video content, combining information to analyze, reason, and solve problems. QVQ-Max supports applications in learning, work, and daily life scenarios, such as solving mathematical problems, assisting in data analysis, and providing outfit suggestions. QVQ-Max demonstrates strong potential in visual reasoning capabilities and is expected to become a practical visual intelligence assistant, helping people solve more real-world problems.

QVQ-Max's main functions

• Image AnalysisIt can quickly identify key elements in images, including objects, text labels, and small details that are easily overlooked.
• Video analysisAnalyze video content, understand the scene, and infer subsequent events based on the current scene.
• Deep reasoning Further analysis of the image content, combined with relevant background knowledge, is needed to make inferences.
• Creative generationCreate role-playing content based on user needs, such as designing illustrations and creating short video scripts.

QVQ-Max performance

In the MathVision benchmark test, adjusting the model's maximum thought length resulted in a continuous improvement in the model's accuracy, demonstrating its great potential in solving complex mathematical problems.

Example of QVQ-Max generation

• Multi-image recognition

• Mathematical reasoning

• Palmistry

QVQ-Max project address

• Project official website:https://qwenlm.github.io/zh/blog/qvq-max

How to use QVQ-Max

• Visit the websiteVisit the official website of QwenChat.
• Registration and LoginFollow the prompts to create an account and log in.
• Enable visual reasoning functionSelect the QVQ-Max visual reasoning model in the web interface.
• Enter the question or taskUpload images or videos in the input box, and describe the task or problem.
• Submit an issueAfter you have finished entering the information, submit it.
• Waiting for model responseThe model generates answers or solutions based on the input.

QVQ-Max's Future Plans

• Improve observation accuracyVisual content-based verification techniques (such as grounding) are used to verify the model's observations of images and videos, thereby improving the accuracy of recognition.
• Enhance visual agent capabilitiesEnhance the model's ability to handle multi-step and complex tasks, such as operating smartphones and computers, and even participating in games, becoming a more powerful visual intelligence assistant.
• Enriching interaction methodsThis allows models to transcend the limitations of text during thinking and interaction, encompassing more modalities such as tool validation and visual generation, thus providing a richer interactive experience.

Application scenarios of QVQ-Max

• Workplace assistance: Assist in completing tasks such as data analysis, information organization, and programming code writing to improve work efficiency.
• Learning tutoringIt helps students solve difficult problems in subjects such as mathematics and physics.
• Life AssistantIt recommends outfit ideas based on wardrobe photos, provides cooking guidance based on recipe pictures, and offers practical suggestions for daily life.
• Creative CreationIt supports artistic creation, such as designing illustrations, generating short video scripts, and creating role-playing content, thereby inspiring creative ideas.
• Visual analysisIt analyzes complex images such as architectural drawings and engineering diagrams to assist in decision-making and design in professional fields.