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InternVL - A multimodal large model launched by OpenGVLab

InternVL is a multimodal large model developed by OpenGVLab at the Shanghai Artificial Intelligence Laboratory, focusing on vision and language tasks. It adopts a ViT-MLP-LLM architecture, consisting of vision modules (such as InternViT) and language modules (such as...

What is InternVL?

InternVL is a multimodal large-scale model developed by OpenGVLab at the Shanghai Artificial Intelligence Laboratory, focusing on vision and language tasks. It adopts a ViT-MLP-LLM architecture, achieving deep integration of vision and language through the fusion of vision modules (such as InternViT) and language modules (such as InternLM). InternVL is trained on massive amounts of network-level image-text data, supports multiple modal inputs such as images, videos, and text, and can generate multilingual outputs.

The main functions of InternVL

  • Multimodal understandingIt can process and understand information from different modalities (such as text, images, videos, etc.).
  • Multidisciplinary reasoning: To perform complex reasoning and problem-solving across multiple disciplines.
  • Multilingual processingSupports understanding and generation of multiple languages.
  • Pure Language ProcessingPerform language tasks such as text analysis, generation, and understanding.
  • Document and chart understandingIt can effectively recognize and interpret text in document images and supports zero-shot learning tasks.
  • Infographic Q&AIt performed exceptionally well in infographic question-and-answer tasks.
  • Scene text understandingIt can understand and process text information in a scene.
  • Science and Mathematical Problem Solving: Possesses strong abilities in solving scientific and mathematical problems.
  • Multimodal hallucination detection: Identify and distinguish between real and fictional visual information.
  • Visual groundingMatch text descriptions with actual objects in images.

InternVL's technical principles

  • Vision EncoderThis employs an improved Vision Transformer (ViT) model, such as InternViT. It is responsible for converting the input image or video into a high-dimensional feature vector to extract visual information.
  • MLP ProjectorIt is used to map visual features to the same feature space as the language model, and the two can be effectively fused.
  • Language Model (LLM)As the base model, it is responsible for handling text input and generating text output, based on InternLM.
  • Dynamic High ResolutionBy segmenting an image into multiple small blocks (tiles) and dynamically adjusting the resolution, the model can efficiently process high-resolution images while maintaining computational efficiency.
  • Pixel ShuffleBy reducing the number of visual markers, computational complexity is reduced while preserving the detailed information of the image.
  • Progressive Training StrategyFirst, a small model is pre-trained on a large amount of noisy data, and then a large model is aligned on selected data, thereby reducing the consumption of training resources.
  • Multimodal input and outputIt supports multiple input modalities such as text, images, and videos, and can generate multiple output formats such as images, bounding boxes, and masks.
  • Pre-training phaseTrain the visual encoder (such as InternViT) and MLP projector while freezing the weights of the language model.
  • Fine-tuning stageUnfreeze all parameters of the visual encoder, MLP projector, and language model and perform joint training.

InternVL project address

Application scenarios of InternVL

  • Visual Question Answering (VQA)InternVL can handle problems related to image or video content and is widely used in fields such as education, e-commerce, and customer service.
  • Document and chart understandingInternVL excels in Document Understanding (DocVQA) and ChartQA tasks. It can extract key information from documents, parse tables and charts, and generate document summaries or chart explanations.
  • Multilingual translation and comprehensionInternVL supports multilingual processing, enabling it to process and generate text in multiple languages. It has broad application prospects in cross-language communication and international business, helping users quickly translate and understand documents in different languages.
  • Image and video analysisInternVL can be used for automatic annotation, classification, and understanding of image and video content. In the field of security monitoring, it can analyze surveillance video in real time and identify abnormal behavior; in content moderation, it can quickly identify illegal content.
  • Intelligent Customer ServiceInternVL can serve as a core technology for intelligent customer service, supporting multimodal interaction. Users can describe their problems by uploading images or videos, and the model can understand and provide solutions.
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